{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_app = pd.read_csv('AppRNN.csv', index_col='Date', parse_dates=['Date']) #导入数据\n",
    "\n",
    "# 按照2020年10月1日为界拆分数据集\n",
    "Train = df_app[:'2020-09-30'].iloc[:,0:1].values #训练集\n",
    "Test = df_app['2020-10-01':].iloc[:,0:1].values #测试集\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler #导入归一化缩放器\n",
    "Scaler = MinMaxScaler(feature_range=(0,1)) #创建缩放器\n",
    "Train = Scaler.fit_transform(Train) #拟合缩放器并对训练集进行归一化\n",
    "\n",
    "# 对测试数据进行归一化处理\n",
    "Test = Scaler.transform(Test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1Vcl53njjDezduxfPPPMMhg4dCl9fX5w9exZnz55Fv379zALdsggNDUVQUBCOHTuGl156CQ0aNIBcLkf//v3RvHnzBz4+ERHAEQ4iqiEKRiyKfktclI+PDx5//HFkZGSY5RW0b98eW7duRXZ2Nr755hvs3bsXPXv2xPbt2xEREWHxeIMHD8bKlStx8+ZNfP311zh58iSGDBmCPXv22FT0z9Z+t2jRAm3btsWNGzdw4MABAECbNm1w+PBhvPTSS7hz5w6WLl2KtWvXIjExEf/3f/8HFxcX0+tnzZqFUaNG4erVq5g/fz4+/fRTbN26tcT+AUDbtm3RtGlT6HQ6s9obhS1evBhvv/02cnJysHz5cuzbtw+PPfYYdu3aZTGnQq1WY/Xq1Wjbti1+/fVXfPbZZ/j0008RExNTbH8Kjtm/f38cPHgQixYtwrlz5/DUU0/h4MGDFTadqoBSqcT8+fOxb98+PP3004iNjcWKFSuwePFi7N+/H82aNcOyZcvMKtsvW7YMCxYsgJ+fH1atWoXly5dDpVLho48+woYNG8yWMe7WrZupRsbWrVuxatUquLm5Yffu3eX2HuVyOVavXo3u3btj586dmD17Nj799FOLq3wREZWVoNFoxJKbERHVXGvWrMFrr72GadOm2Vz8bNasWZg9ezaWLFliqvFBRERE5jjCQUREREREFYYBBxERERERVRgGHEREREREVGGYw0FERERERBWGIxxERERERFRhGHAQEREREVGFYcBBREREREQVhgHHQyw6Orqyu/BQ4HW2L15v++L1tg9eZ/vhtbYfXmv7qszrzYCDiIiIiIgqDAMOIiIiIiKqMAw4iIiIiIiowjDgICIiIiKiCqOo7A6UhV6vR1ZWVmV3o9pzdHREWlpaZXdDwsXFBQpFtfxYEhEREZEF1e7JTq/XIyMjA2q1GoIgVHZ3qjWVSgVHR8fK7oaJKIrQaDRwc3Nj0EFERERkZxk6I1ZEZUEmCBgQ7IgQt/J5Hqt2T3VZWVkMNmooQRCgVquRnp4ODw+Pyu4OERER0UMjNdeIfn8k4nKaHgAw81QavuriiScbOD/wsatlDgeDjZqLv1siIiIi+8oziHhmX7Ip2AAAnRH4+K/0cjl+tQw4iIiIiIjowaXnGfHcgRQcicsz23cz04D0POMDn4MBBxERERHRQ2rCoVRsu6m1uv9aut7qPlsx4KjGYmJioFarcebMmQo/14ABAzBlypQKPw8RERERVbwT8bnwW3EH229ZDzYABhzV0tmzZ+Hl5YV+/fqV6nWWHviDgoIQFRWFFi1alFv/1qxZg9q1a5ttX716Nf773/+W23mIiIiIqHJk6ox4am8Kis6W8lLJMDBYuoLpquhsiKL4QOdjwGFnq1atwosvvoiLFy8iKirqgY4ll8vh7+9vlyVkPT094ebmVuHnISIiIqrp7mYbcD1d/8AP8mX185VspOSa52bM7uCB7oEqybb9sbmYfTbjgc7HgMOOcnJysGHDBjz33HMYPHgwVq1aJdl/8uRJDBo0CIGBgQgODsagQYNw9+5dTJw4EUeOHMG3334LtVoNtVqNmJgYyZQqo9GIZs2aYdmyZZJjXrlyBWq1GmfPngUA/O9//0OnTp0QGBiI1q1b44033oBGowEAHD58GK+99ppp6WG1Wo1Zs2YBMB9h0Wg0eOWVVxASEoKAgAAMGTIEFy9eNO0vGCk5ePAgOnbsiMDAQAwcOBA3btwo/wtLREREVE2sv5qNFuvj0OaXeLx/snxWgdIZRRhtDF5EUcTyS+YFtN2VAgaGOKGBu/kX2V9dyLT5+JZUuzoc1qh/uGPX82meN592VJItW7agTp06aNasGUaNGoXnn38eM2fOhFKpxPnz5zFo0CCMGjUKn376KVQqFY4ePQq9Xo/PP/8cV69eRWhoqGlak4+PD27fvm06tkwmw/Dhw7FhwwZMmDDBtH39+vVo3LgxWrdubWo3a9Ys1K1bF1evXsX777+PqVOn4ptvvkGHDh0wa9YsfPzxx6a8EBcXF4vvZeLEibhy5Qp++uknqNVqfPzxxxgxYgROnToFJycnAEBubi7mz5+P//3vf1CpVJg4cSL+7//+D5s2bSr1tSMiIiKq7gxGES8fSjX9vOxCJqa1doO7Q9nHAHbcysFbRzQwiMD8jmoMrutUbPu4HCMuaczzMj4N94CTQkB9CwFHep6Iy2l6NFEry9THGhNwVAerVq3C6NGjAQBdunSBk5MTtm3bhiFDhuDLL79EixYtsGjRIlP7xo0bm/6uVCrh7OwMf39/q8cfOXIkvvzyS1y/fh316tUDAGzcuBFPP/20qc2rr75q+ru/vz8++ugjjBkzBl9//TUcHBzg7u4OQRCKPc/Vq1exfft2/PHHH+jcuTMAYNmyZWjRogU2bNiAsWPHAsivCj9v3jyEhoYCAN544w28/vrrEEWR9TaIiIjooXMiQbr0rF4EkrTGMgccoihi+ok0xOfkT49680gqHg1yhKPC+nPW38nSPtRxlWPfQF/4OskBAMGucjT2UCAqTRqUnErMK3PAwSlVdnLt2jUcP34cI0aMAJBf4G7kyJGmaVXnzp1Dt27dHugczZs3R1hYGNavXw8AOHXqFK5fv44nn3zS1ObgwYMYOnQowsLC0KBBAzz77LPIy8tDfHy8zeeJioqCTCZDeHi4aZuHhwfCwsJw6dIl0zaVSmUKNgAgICAAeXl5pilcRERERA+T32JyzLZl6cs+VSk114gbGQbTz5o8EXP/Ti92+tO5ZJ3k50eDHE3BBgDIBAE/9fY2e93pRPM6HbbiCIedrFy5EgaDAc2bNzdtK0gUKjw16kGNGjUKq1atwrRp07B+/XpEREQgODgYAHDz5k2MGjUKY8eOxbvvvgsXFxdcunQJL774IvLyyv4hKqzwyEXRZPaCfUbjgxeQISIiIqpudt/ONduWpSvdc1GmzogfLmUh1wi09TEfcfjiXCY0eSK+6Ki2+Pq/iwQcLb3Mj9HAQ4ENfb3x5O5k07ZTiTqzdraqMQFHWXIq7EWv12Pt2rWYOXOm2XK4EyZMwJo1a9CyZUscOnTI6jEcHBxgMBis7i8wYsQIfPjhhzh58iR+/fVXvPfee6Z9Z86cQV5eHmbNmgW5XA6tVov9+/eX+jyNGzeG0WhEZGSkaUpVeno6Lly4gDFjxpTYRyIiIqKHTY5exFULNS2ySznCMeFQKv4oplAfAHx3KQsqOfBhew8oZdLpVUUDjlbelqdJtSsSzERpdDAYRchlpZ8WzylVdrBz504kJydj3LhxCAsLk/wZPnw41qxZg9dffx3nzp3DW2+9hfPnzyM6OhorV67ErVu3AADBwcE4ffo0YmJikJycbHWUoHbt2ujcuTMmT56M9PR0DB061LSvQYMGMBqN+Oqrr3Djxg38+uuv+PrrryWvDw4ONgUiycnJyM7ONjtHgwYN8Pjjj2Py5Mk4evQo/v33X7z88stwc3OTTN8iIiIionzRaTpYCi1snVIliiI2XM0uMdgo8NW/WXj9z1TJthStAbez7n+xrBCApp6WAw4vRzl8He+HCnlG4GZmyV9+W8KAww5WrVqFrl27wsvLy2zf0KFDcfPmTSQnJ2Pz5s24fPky+vbti969e+OXX36BUpn/IXjjjTfg4OCAiIgINGjQwBSIWDJy5Ej8888/6Nu3L9RqtWl78+bN8fnnn+Orr75CREQE1qxZg48//ljy2g4dOuCFF17Aiy++iAYNGkiS2Av76quv0LZtWzz11FPo3bs3cnJysHHjRtMKVURERESU72qaHt22JlrcZ22EI1NnRHSaDmn3qvMtPJ+Jlw6lWmxrzcZrOdAWOv65FOnoRhNPJVRy6yMWDT2kk6GKvt5gFJFrKDlgEjQaTeVUHCmjtLQ0eHh4VHY3agStVgtHR8eSG9pZTfsdR0dHS5LnqWLxetsXr7d98DrbD6+1/TxM1/rR3xMRaSXpen5HNV5oIi1D8FN0FiYd1SDPCMgE4MUmLvjhUhbKkl9+dKgfwjyViI6OxjZtAGaeul/74+lQZyzp4mn1tW/8mYpV0dLZLj/38cJjdZyQqTNiyb+ZmHUmo8TUBo5wEBEREVGl0upFXL6XI1DTXEjVWQ02ACBLL50mv/+OFq8fyQ82AMAoAt9etC3Y2DXAx2xbdKHlbYuuUGUpYbywUA/zdO/3ItOw4Wo2wtbFYdYZ2yqQM+AgIiIiokpzWaND8w1xCP81Af23JSGjlKs2VXU/XzHPhy2s6JSqmafSUda4q7FaiZeaSkdLrhQKOGxNGC9QdEoVAFxNN+ClQ6lI19neSQYcRERERGR3RhFYdTkL4b8mIEmbH2REJubho9PpJbyy+jCKIn65Zl57o7DsQg/umTqjWZ6ErUI9FPBwkCG0SKXw6LT842XpIVklSwDQvAwjHGXBgIOIiIiI7G53khxvHNGYbf/2YhaOx5vXq6iO/krS4U528Ss7FR7huJJmvmxuUZ+Fe8BSIfHBIfl5uUWDhCv3gozoLJlklayGHgq4KosPBeq7Kcol6GDAQURERER2dzhFbnXfm0c0yDWISMwxYN8drU0rIVVFG65Kp1MNqeuIxZ3Vkm2ZhQKOqBICDkc5MLaRM4bVM18VdFBI/rai06Aua/S4kqbDS+elCwWVlL8BAHKZgJU9zVdZLa1qGXCIxZRrp+qNv1siIqKHQ0Ku9eVYL6fp8fYxDZqui8MTu5LRbUsC8qpZ0HEqMQ/fXcqSbBsU4gSXIsMT2YWSxqM0xU+nauPjAFelDLM7eKCB+/2ArbW30pSPUdtFDg+H++dI14lovynB7Fgl5W8UaOqpxJnh/lb3N/MseQSk2gUcLi4u0Gg0fDCtgURRhEajgYuLS8mNiYiIqFpLzCu+YvXq6GzTykxRaXpsuVF8LkRVM/tMumRlKXelgEeDHOGsLBJw6EQYRRFH43Ix/1xmscds5+MAIL8o347HfTExzAUvNXHBql5eEIT848oEASMbOJfYP1sDDgCo6yZH5wAHs+3TWrvhyFDrwUiB8skEsSOFQgE3Nzekp9echKLKkp6eDnd398ruhoSbmxsUimr3sSQiIqJSEEXRLOA49YQfum1NtFoI74eoLDxpw4N0VfFXknS0YmZ7d7g7yOCskH7fn6UXMf9cJj75q+Rn2/a+9x/6fZ3kmNVBbbHdy01d8O3FLIv7CrT0Ng8grBEEAT/19sabR1Kx5YYWShnwapgrprRys+n1Nj3ZZWRk4NNPP8Xvv/+OpKQktGzZEp9//jnatm0LIP9D8/nnn2PFihXQaDRo164d5s2bh6ZNm5qOodFoMHXqVOzYsQMA8Nhjj2HOnDmSSti2UigUNaowXGVJSEhAnTp1KrsbRERE9JBJyxORa7wfcDgrBDRwV+CVMBer3/LnVKMpVTqjiORc6fK+4xrlz+BwLTKlKlMn4st/zOtZqORAbpF88zY+to1KhHoo0cnfAUfjLdf/6OjvAE9V6SY6eTjIsKKnN25n6uGqlEFditfb1PLNN9/Evn37sHTpUhw9ehQ9e/bE0KFDERsbCwBYtGgRlixZgtmzZ2Pfvn3w9fXFsGHDkJFx/+KNHz8e586dw8aNG7Fx40acO3cOEyZMKNUbJSIiIqLq726RlZsCnGQQBAFvNHeDu9LyVKuLqTroqklhwMQcabDh4yiDQpb/vopOqTqXokN6nvn7ereNO54JvT+i072WCsGu1hPtiypIIrdkaVfr1cVLEuSqKFWwAdgQcOTk5GDr1q2YOXMmunbtivr162PGjBmoV68evv/+e4iiiKVLl2LSpEkYMmQIwsLCsHTpUmRmZmLjxo0AgKioKOzZswcLFy5EeHg4wsPDsWDBAuzcuRPR0dFle7dEREREVC3FFQk4arnkP0h7qmR4vbmrxddoDflBR1WjN4pYeyUbC85lICEn/30V/LeAn9P9R25nS2vaFhE5zA9vtXDDgk5qLOioxmfhHljd+36ehi0GhTha3D6phSvqutl3+nqJAYder4fBYICjo7TTTk5OOHbsGGJiYhAfH49evXpJ9nXq1AknTpwAAERGRsLV1RUdOnQwtYmIiICLi4upDRERERE9HGKLBhzO97+5f7O5Gxq6W34gPpNU9QKO2WczMPFwKj48nY7hu5JhFEUkFBnh8HO6//6KrlJV1LTWbmikzp86pZQJeL6JC15t5gq3EmpmFBXkqpCsZFWg8LW2lxLDGzc3N4SHh5tyMvz9/bFx40ZERkaifv36iI+PBwD4+vpKXufr64u7d+8CyM8V8Pb2lkRlgiDAx8cHCQnmy3QV4OhHxeM1tg9eZ/vi9bYvXm/74HW2H17rivfvLQWA+0nLjrnpiI5ONv383/oC3vzHERq99OF8/9UkdJLF2qubJcozAnP/vj/t6XyKDtvOXkVUpgyAyrTdSZeF6OhUAEB+aof15PdaeYmIjo4vl/41cHDA1SKP+0JaAqKj75bL8QuEhoYWu9+m8ZRly5bhtddeQ1hYGORyOVq1aoURI0bg7Nmz5dFHq0rqPD2Y6OhoXmM74HW2L15v++L1tg9eZ/vhtbaPvCQNgPurKDWt7YPQ0PtTqUIBdG9mxIqoLPz31P3Vm67qnBAaGmK/jpZg5y0tgGTJtlyPQAhyA4D7/W7gp0ZoaP6CR6IoAkctB00yARjcqi68HMtnFKKrNgO7kqSrX7VrGIRQX9tXqCoPNo3N1KtXD9u2bcOdO3fw77//Yt++fdDpdKhbty78/fPX3k1MTJS8JjExEX5+fgAAPz8/JCcnS2pniKKIpKQkUxsiIiIiejjczJRW1K5tYZqPh4MMT4dKRwIupOqQY2XZ3MqwNca8Nsj1DAPii0wZ8y+Uw1FcHsbgEKdyCzYAy0vfBrrYf0pVqSaDubi4ICAgABqNBnv37sXjjz+OkJAQ+Pv7Y//+/aZ2Wq0Wx44dM+VshIeHIzMzE5GRkaY2kZGRyMrKkuR1EBEREVHNYBRF3MkyWCzWHJMhfSAPcbP8EOztKEdIoZWZDGJ+Be+qQJNrxObr5gHH1XS9eQ5HkYCqjoXVpmQCMKONbXUtbGWpuJ+fo/3rftt0xr1792L37t24ceMG9u/fj4EDB6JRo0Z4+umnIQgCJk6ciEWLFmHr1q24cOECXn31Vbi4uGDEiBEAgMaNG6NPnz6YPHkyIiMjERkZicmTJ6Nfv34ctiQiIiKqYRJzDGj/SzyarY/DgO1JyDVIZ7nczCwScBSz3Gv7ItN/FluoWVEZVkdnIcvCaMu1dD0StEVWqSrykD+8nnTJWpUc+DzcA43Vtlf/toVaJZOc6+lQZ8hltq90VV5syuFIT0/Hhx9+iNjYWHh6emLw4MF4//33oVTmX5S33noLOTk5mDJliqnw36ZNm+Dmdj9KW758OaZOnYrhw4cDAPr37485c+ZUwFsiIiIioso052wGrt0bxTgan4fvL2VhYrP8HI2EHKOkiJ+bUii2CN1TDZ3xS6GRhF23c3EhVYcwz/J9OC+tLTfMRzeA/IDDpUitDV8naUD133buaOPjgLQ8IwaHOMFZIcBBXjGBwNfdPNE3yBEJ8XF4tWNghZyjJDYFHMOGDcOwYcOs7hcEATNmzMCMGTOstlGr1fjmm29K30MiIiIiqjaMoohvL2VJts06k24KOGKK5G8Eu8qLzWvoXVuFcF8HRBaaSnU6Ma/SA46iozQFErVGJGrv/ywXgLpFpozJBAFD6lovzFeelDIBoxs6I1o0mIoP2pt9q34QERERUY0WmWCeY5GuE9F6YxzCPJXYdlMr2RdSQhE6QRDQOUAacNzOsvywX5FyDSIWns/AnSwDJjR1RZLWWPKLALTwUsK1lDU0ahoGHERERERUbn6L0VrcfiPDgBsZ5oFCcfkbBYKKtKmMgOP9yDTTyM36q9kw2LhYVoS/fZegrYoe7nCLiIiIiMqNKIoWl4otTkkjHAAQ5CJtc9vKdKaKosk1SqaJaUtx+o7+qpIb1XAMOIiIiIioXPydrMOtIsGAopi0AbkA9K/jWOJxg1yKjnDorbS0TabOiOg0ncUley2xliBeINzXAd5WEt8fsXORvaqIAQcRERERlYs/iuRnPB7siNvPBGJLPx+MbSQt4tfa3YDfHvOxbYTDwpQqW4OFoiITctFw7V08sikB/bcl4XZmycHLryUEHAHOMnSwMHXKSyVDLWc+bjOHg4iIiKgKy9GLiEzIw9V0PU4n5aGDnwOeDXUudmWnynIwNlfy88BgRzgqBHQPVKF7oAqvNnPF38k69AxUIe32NYQG2DbdyMNBBnelgHRdfpCRawCStEbJcrOxWQbcytSjna+D1dWYRFHEpKMa05So4wl5GLA9CSeG+cOxmKGYKI2u2P75Ocnh5SgzS4hv6qmokr8ne2PAQURERFRFaXKN6PN7Iq6k3/8Wfk10NpwVAkbUvz9iEKXRYcctLTr6OyDcr3JyBjJ0RpxOkq5Q1SNQOl2qiVqJJveK26WV8vhBLnJc0Ny/DrezDKaAY/8dLZ7Zl4IsvYjW3kpsetQbXo7myeiH4/JwIVU6ohGTacDppDx0thL8aPUi7mYXvyKVr5MMTS0U7Qsr50J+1RXHeIiIiIiqqAXnMiTBRoHV0dmmv9/JMqD3b4mYeSod/f5IwvH4XLP29vBjVJZk5aZQDwUCXUpegcpWRadVFdTB0OQa8crhVFPV77PJOjx/INXilKvvLmVaPHaslVWv0vKMePFgSol983WUo4WXeXDRxJPf7QMMOIiIiIiqpPhsA765mGVx3/H4XOTee7pfcTkLmfcetkUA889l4EhcLmac0GDrjZwy5zqUxv47WvznZLpkW7da5TvSUjTX40pafiC2+J8MxOdIRyAO3s3F2WTpNKjUXCO237S8ZG9cjuWA4/Mz6WZ5KZb4OskQ4mYeXNW3IT/lYcCrQERERFQFLb+UhRwrxR60BuBkYh7+TdFhztkMyb5dt3Ox63b+KMfSC1kY2cAJX3f1hKyCcgluZeoxbr/5KMCweuVbSTvUXfrYGp2WH1AciLU8onMxVYc2PvcTuTddz0aelZlRcVamTC29YDngK8rPUXavergjttzID1C8VJYTyR9GDDiIiIiIqphcg4gfoop/2H0vMg1/JxefzAwA66/moEuACmMbuZRX9yS+vZhlSuYuMLW1G7rYmBBuq1CPIiMc6XoYjKJZTkaBoqMeu25ZH6mItzDCkZprWyVxAAhwzh/dmBWuhkJIQ5LWiCmt3eCs4GQigFOqiIiIiKqc32JykKS9/8DrrhQwL8JD0saWYKPAhqvZkp+NoogUrQEG44NPtzoSJx1heKu5K95t4/7Axy2qoUfREQ49rqbrrY4CxWVLg4hbxVQnL9oWAM6n2HZ95QJQ+16uSqCLHN/18MKWx3zKPeCqzhhwEBEREVUxP1+RBghjQp0xor4zVGXMwT4cl4fL95Z2TdIa8Pi2JNRfG4devydCU4pv8ovK0hnNAp9Xm7mW+XjFqe0ih5P8/rSwtDwRB+9aT5AvmpdROIAza2thSpWtAUeQi9zqMryUjwEHERERURUSpdFh7x3pg/QzoS5Qq2QYEFz2vIjwXxNw+G4untqTjOMJ+cvX/p2sK3HqVnFOJ+mgLzTAUN9NDn/n8luZqjCZIKBBkVGOKcetL64bXyiIMBjFYgMOS1Oq/rEx4LClcOHDjgEHERERURVxJU2HMXuTUXiSUAsvJZrfW3K1aLXuAn5Otj3SDdqRhJOJ0gfpguCjLIouwduxgqcRNVHb/nB/t9A0qV23tSg8e8zdQYCy0CXL0InI1EkDkktFiv018rB87hDXigmwahIGHERERERVQHqeEUN3JuNquvTb9pea3k/27l5LhWdCpUHH0LpOuDy6FmqXcWTh76SyBxx/JUkfysN9K3ZVpv51HEtudE98jgGiKGLTtWw8tVe6ipa/kxz+TtLrNXZfCn69nm3Ka7mdKf09/PKoN3oEmgdUHOEoGQMOIiIioipgw7Vs3C6S2Dy8npMkwBAEAQs6qTGhqQu8VTL0r+OIhZ3UAIBpbdwkr32npRt2PO5T4nnjcowWk6Zt8XeyNFhp7VOxlbUfD3aCu9K2fAmtIT/PY+F582J/vo4y1HKWPgbvi83F8wdSMXJPMnINIhILTcGSCfkrUQ2taz6lLZgjHCViwEFERERUBRwqkgAd5qnAVxbqZyhlAmZHqHF1TC2s7eMNtSr/cW5MQ2f0uvcNfJhagdebuyLCX4UDg3yhKOEZvTQrXhWIzzbgbqE8CQcZ0FRdsQGHk0LAkw0sTytTysynN8XlGHDOQi6Gn5MctayMCO29k4utN3Ik2/ydZFDKBIsBB6dUlYxjQERERFStJeQYsCIqC54qGZ4JdYFjSU/XVczcs+n4PipL8vAOAIs7e0Ilt/29KGQCNvXzQbLWAC+VDMK9QKW1jwO2P+6Lxf9k4JJGj661VMjSGfHz1fsP1aeT8tDPxulKB2NzcSBWiwVFRg6aeSnhUIr+ltV/27ljRVSWJFkdyF/FqpazHDGFpkIVVCMvytdRhuJKZPxaJOAoWPZWrZJhfBMXLL+Un2hfx1UuKS5IljHgICIiomorQ2fEY38k4lpG/kPm5TQ95kSoK7dTNkrSGvBuZBrWX80x2+fuIKCVd9lGC7wdzb9xf8TPASt7eZt+XhOdJQk45pzNwLG4XLTwVqK1twM6B6hMD9kFRFHEO8fT8N0ly6tatS5jf0vLw0GGg4P90HlLgmR7kIt5Xoa1ZXN9nWRwLiYw3XZTWiQwsNBoyKfhHvB3kiE224AJYa52CbKqOwYcREREVG19dCrdFGwAwKrL2ZjZzh0u95YgEkUR/6Tq4aWSmT1AW3MxVYeVl7MQ4qbA841dSjXKUMBgFHE+RYdaznLE5Rjg4SBDktaIem5yeDvKkZprRJ/fE3Ejw3LuRCd/VYXWduhayzz5+XBcHg7H5QHIgkLIrxY+IcwVHg7513LtlWyrwQYA9Ai0PaH7QYW4mf8uA53lZr/jA7GWAw5XpUwSRJSk8HFVcgFTWpd/YcOajAEHERERVUvH43PxbZEH4ByDiNqr76J3bRW+6KjGR6fTsel6DmQCsLSrJ0ZZmf9fQKMDntmZhPic/OlNqblGzChl1ey0PCNG7k7GCQvLzTrKgW+7e+HtYxok5FivC9HRv2Kn6QS7KlDPTY7rVgIevQh8diYDK6KysamfN9Zdzcb8c+bJ1wUeDVJhcIj9Ag5Xpfl8KEEA6riaVyO3xEEGmwNQoHRtyRwDDiIiIqo2jKKII3F5yNAZ8W6k9aJve+/kovXG+EKvAyYf1SDCz8FsGVODUcTxhDzUcZVjfazSFGwAwE9Xsm0OOP6My8UXf2dgv5Vv1YH8lZOe3ZdidX+BdhW8vCwAdKulwvWM7GLb3Mk2oMOvCcW2AYD1fUteDauiOckFm1aMEgD0q+MIuWD7CBIDjgfDgIOIiIiqBVEU8cKBVGy+YZ7zYItsvYiPTqfjux5eWHc1G7PPpCPAWY6EHCOupBd8Ey7NQ7iVacDtTD2CXIt/ZIrLNmDk7mRkF81kLiN75EM81dAZKy4XH3DY4s3mruXQm9J7vrEzfoi63/9nG7nA1YYlc+d19EAdV4Wp3oYtio6cUOnw6hEREVG1cDIxr8zBRoHtt7Q4GKvFhEOpACDJ/7DmREJeiQHHrtvacgs2AMtThspbhL8KK3p6YdvNHKyzkLhuCzelgBebuJTcsAK82dwNR+LycDlNj+cbO6OtjxI5huJ/B/886W/6XcptzJEJdJaVOYGf8rEOBxEREVULX1+wnLDc0d8BV54KwFdd1CVW287WixiyM7lU5z0WX3IlbmttQlzlGNPQGVv6eVtcFWlMQ2f8+qi3ZJs9RwyG1HXCsm5eWHSveGCBj9u7I9DZ8mPijDZuOD7MD/M7qnF4iF+lVdqu567A8WF+SBwXiAWdPCEIApwVMvg6Wu63k1xAYJGpUUE2TJXa1M8HygpM4H8YcISDiIiIqqQjcbn4+HQ6lDKgb5AjNl03/xZ+cgtXTGntBmeFDGNCXTAgxAmtN8YhNbf8Rht+i8nBZ+EeVpc/XXc1G2uvSKcmDavrhGXdPCWv+f0xH/x0JRvpOiMi/FR4rrGzqajf5Bau+D4qC219HPB/LaUVw+3hmVBnHInLxfZbWjwa5IiJzVzhqpRh8jGNpF1HfwdMu7dCU5MKLvJnC5kgoGgsUMdVLqkSXqCeu9ysiOKH7d0x/mAqrH1afBxlVeJ9VnclBhwGgwGzZs3C+vXrER8fD39/f4wcORLTp0+HQpH/clEU8fnnn2PFihXQaDRo164d5s2bh6ZNm5qOo9FoMHXqVOzYsQMA8Nhjj2HOnDlQq9UV886IiIio2so1iHjxQAri7iVw5y/Xel+wqxx/j/A3Fbcr4OEgw9re3ph/LgM+jnLMbOeOEwl5GLu/5ERta+JzjNhyI8dihetVl7PwxhGNZJtSBizpqjYLUNr6OqCtlWTwme098N927mbvx17kMgHfdPeCKIqmPjzbyBkro7NwJul+pe7uFpbTrWpaezvgryTz6uIN3c0fe4fXd0YDdwViMg3wdpRh4PYkyf4GFl5DpVfilKqFCxdi+fLlmD17NiIjI/H555/j22+/xfz5801tFi1ahCVLlmD27NnYt28ffH19MWzYMGRkZJjajB8/HufOncPGjRuxceNGnDt3DhMmTKiYd0VERETV2rlknSnYsKSdj4PVh/MIfxXW9/XBV1094e8sR/dAFR70MX79VfPk6gupOvxfkREAAGjlrYRzcWWsraisYMNaHxQyAUu7esLPKf+9+DjK8EIl5WuUxuvNXWGh9iEaelgOHlr7OGBIXSeLwUU9C/U+qPRKDNsiIyPx2GOPoX///gCAkJAQ9O/fH6dPnwaQP7qxdOlSTJo0CUOGDAEALF26FKGhodi4cSOef/55REVFYc+ePdixYwfCw8MBAAsWLED//v0RHR2N0NDQinp/REREVA2dSiw+b6Kpp+3fPHs4yNDMS4l/Usy/9bbVpSL1HIyiiDePpEJnISYa36RyVm2qCE3UShwZ4od/UnRo5+sAd4eqn/5b312BGW3cMfNUumR7SaMVfhZyP4rmfFDZlPipiYiIwJ9//onLly8DAC5duoTDhw+jb9++AICYmBjEx8ejV69eptc4OTmhU6dOOHHiBID8oMXV1RUdOnSQHNfFxcXUhoiIiKjA6aSSAo7Szavv6PdgdS3uZBmQV2gFpBMJeTiVKA1gOvo7YEs/b4xuWHxxwerG10mOnrUdq0WwUeC1Zq5mK0u1L6G2iaVVqxzLUGWezJX49cCkSZOQmZmJDh06QC6XQ6/X45133sH48eMBAPHx+UV1fH19Ja/z9fXF3bt3AQAJCQnw9vaWDNMJggAfHx8kJFgvJhMdHV36d0SlwmtsH7zO9sXrbV+83vbxsF3nY7GOKO57UZe0O4iOtj0xvK4oB2B7/kGQoxG5RiAxL78PRhE4/O9VBDvln3PzTQWA+w+w4WoDvgzVAFkaPGS/qgdSkZ/rD+sJmJHrgBvZMjxTWw9Z4g1EJxb/mk6eKhxNvT+q0UZIQHR0fDGvqF4q6nqXNFupxIBj06ZN+Pnnn7F8+XI0adIE58+fx/Tp0xEcHIyxY8eWW0ct4VSrisXpbPbB62xfvN72xettHw/bddbkGnHnz7vFtunevIHNdRQAQB1kwCdX4k11GvycZEgolCPipZLBx1GGy/emTk1s6YnfY3KQWGi5W9ErCA1rq/DR6XQsu5kpOf7TYd4IDa36+Q1VSUV/rkMBHGtRute875aLMXuTockTMa6RM/q2ql0hfasMlXkfKTHg+O9//4vXX38dw4cPBwA0a9YMt27dwoIFCzB27Fj4+/sDABITE1GnTh3T6xITE+Hn5wcA8PPzQ3JysmTlA1EUkZSUZGpDREREBADX0qX5EvXd5JDLBETfCwZGNnAqVbAB5E8LWtRZjVln0lHLWY6vu3li6vE07LilBQB82VmNlt5KfHPyFtrXDcDAEEf8k6KT1Ne4nqHHjlsiFpzPNDt+hP+DTdmiqqFTgArnngxApk5k/kY5KjHgyM7OhlwuveByuRxGY/63AiEhIfD398f+/fvRtm1bAIBWq8WxY8fw0UcfAQDCw8ORmZmJyMhIUx5HZGQksrKyJHkdRERERNczpAFHqFqJj9q74+sLmXBTyvBmi7IlZY9s4IyRhZa2/am3F47F56GOqxzB96pPjwvSI7SeEwDzFYqupeux4rL5alVeKhkaWVkBiaofdwcZ3Bk/lqsS/3U89thjWLhwIUJCQtCkSROcO3cOS5YswejRowHk52JMnDgR8+fPR2hoKBo2bIh58+bBxcUFI0aMAAA0btwYffr0weTJk7Fw4UIAwOTJk9GvX7+HaoiYiIiISnY9wyD5uZ6bHI3VSizo5Fmu55EJAjoHWM/rqFekgvZSK5XOewSqqsSStkRVVYkBx5w5c/Dpp5/i7bffRlJSEvz9/TFu3DhMnTrV1Oatt95CTk4OpkyZYir8t2nTJri53a+UuXz5ckydOtU0Nat///6YM2dOBbwlIiIiqq5OJuThk7+ky5kWffC3lyY2rITVVK3Ae23c7dAbouqrxH/Bbm5u+Pzzz/H5559bbSMIAmbMmIEZM2ZYbaNWq/HNN9+UrZdERERU4+UZRDxnoSJ4ZQUczT0V6ODngBMJ5kv0ygTgxDA/hHqUbnleoodR9VlQmYiIiGq0Y/G5uJNtMNtez71ykncFQcCn4R4W9w0IdmSwQWQjBhxERERUJey+nWtxe0FCd2Vo56OE2sE8P6PDAxYSJHqYMOAgIiKiKmH3ba3ZtpZeSqgqsdqzIAho5W0eXFjaRkSWMeAgIiKiSndJo0NUmnQ5XDelgE+sTGmyJ0tL3rb05nQqIltx0WgiIiKqdD9fkda36F5LhV/7eUNWBZabDbOwWpWHA7+zJbIV/7UQERFRpRJFEeuvSgOOpxo6V4lgAwCG1nOCp+p+X95p6VZMayIqiiMcREREVKluZRkQm200/eysEDAwxLESeyTlqZJhTS9vLL+UhYYeCkwqY6VzoocVAw4iIiKqVFeK5G4081TAVVm1JmF0ClChUzFVyYnIuqr1r5mIiIgeOtFFAo6GrG9BVKMw4CAiIqJKdSVdGnCEWlgVioiqLwYcREREVKmKTqlq4M6Ag6gmYcBBRERElarolCqOcBDVLAw4iIiIyG6StAak5eWvSLXvjha+K+7gdpbBtF8AUN+NAQdRTcJ/0URERGQXS//NxPsn0wAAn4Z74LMz6dAZpW1aeCnhqKga9TeIqHww4CAiIqIKp8k1YuapNBjE/J+nn0iz2O5N1rggqnEYcBAREVGF2XtHi7lnM3A8Ia/Eti29lHiinpMdekVE9sSAg4iIqIa4mKpDSq4RHfwcoJBV7rQkg1HE52czMPfvDJva966twv+6eEImcDoVUU3DgIOIiKgGWBGVhbeOagAA/eo44qdeXpBXUtCRrDXgpYOp2Beba1P7GW3cMK21ewX3iogqCwMOIiKiaiw9z4gZkWlYE51t2rbzlhbfXsrCK2Fly4e4nanHJ3+lIypNjzyDiIYeCkxv7Y6mnpYrgGfpjDgSlwe5DPBxlOHpvSmSlacs8XAQ8FRDZzzV0BmtvB3K1E8iqh4YcBAREVVjrxxOxbabWrPtn5xOx+gGzlCrLK+An6kz4rtLWUjPM+L5xi4Icr3/SDD+YKok5+LfVD1+i9Hi+FA/uChl0BlFeKpkWPJvJn67kYOLGr2lU0j0C1IhXSfCQSbgk3APNPZQwEHO6VNEDwMGHERERNXUJY3OYrABAJl6EfP+zsAn4R4W9//fMQ3WX80BAOy5k4t9A30hlwk4n6KzmOBtFIHwXxNK3ccIPwf80NMLtZzlpX4tEdUMDDiIiIiqIVEUMetMerFt/vdvJgJd5Hi1mSs0uUYsPJ+BlFwjApzlpmADAP5O1uFwXC723snF4n8yy62PLzZxwecdPKCs5AR2IqpcDDiIiIiqmcQcAz7+Kx1bblge3Sjs3cg0NPNU4PuorGLbD92ZXJ5dxNOhzpgX4QGBq04RPfQYcBAREVUj++9o8ey+FGTqRbN9vz7qjd13tPjq3yzJ9uG7kmGhebma0soNKblG6IwiBgY7oW+QisEGEQFgwEFERFStfHom3SzYUDsI2D3QF6EeSnSrpYK7UobPz96vf/GgwUYjDwUup0kTwzv6O0ApEzCznTtaeCmZAE5EVjHgICIiqmSiKOLPuDzczNSjfx1HeDlaTrDONYg4m6STbPN3kmFDX2+EeuQvWSuXCZjexh2puUYsu5hl6TAlGlnfCe+2dYeDTECuQUQ9dwU0uUb8799M6AwiJjZzRQCTwInIRgw4iIiIKtnKy9mmon3NvZTYPcAXTgrpiEGGzogXD6SYjVacGu4PN6X50rf/aeeObbe0uJUprYehkgMXRgbA21GOry9kYvqJNMn+Fxq7YH4ntdnx1CoZ3m/L4nxEVHqWF+cmIiIiu1l4/v70p39SdNhwLduszUen07HrtrRy98BgR4vBBgC4KmX4tpsnHIrs/qCdB7zvjaCMqO+EInENRjd0KsM7ICKyrsSAo0WLFlCr1WZ/Ro4caWqzfPlytGzZEv7+/ujevTuOHj0qOUZubi6mTJmC+vXrIzAwEKNHj8adO3fK/90QERFVM1fT9LieIR2FWHguA6J4fyjDKAK/XMsp+lK09im+QneEvwo/9PCC2kGAXABmtHHDK2Eupv0+jnKMb3r/556BKjziy6rfRFS+SpxStX//fhgM92+EcXFx6NGjB4YOHQoA2LRpE6ZPn44vvvgCERERWL58OZ588kkcP34cderUAQDMmDED27Ztw3fffQdPT0+89957GDVqFA4ePAi5nHNAiYjo4bXysnmexbUMA84m67D3Ti7WXsnC1XRnAEazdq29lSUef0CIE/oEOcIginBWmH/P+FF7D3Twc0CGTsTQuk5cWYqIyl2JAYePj4/k51WrVsHNzQ3Dhg0DACxZsgRjxozBuHHjAABz587F3r178f3332PmzJlIS0vDqlWrsGTJEvTs2RMAsGzZMrRo0QIHDhxA7969y/s9ERERVQvH4nOxyEqhvZ6/JRb7WrWDgHA/20YjVHIBgOVAwkEuYFg9Z5uOQ0RUFqXK4RBFEatWrcKoUaPg5OSEvLw8nD17Fr169ZK069WrF06cOAEAOHv2LHQ6naRNUFAQGjdubGpDRET0MHo/Mq3kRlas6OkN96IJGkREVVCpVqnav38/YmJiMHbsWABAcnIyDAYDfH19Je18fX2RkJAAAEhISIBcLoe3t7fVNtZER0eXpntUBrzG9sHrbF+83vbF611617MFzLzsgIuZZZtW/HVzLQKzboKXvmLwM20/vNb2VVHXOzQ0tNj9pQo4VqxYgbZt26JFixYP1ClbldR5ejDR0dG8xnbA62xfvN72xetdepuuZeONcxpklbEaXxsfJUa0C4RCxlyLisDPtP3wWttXZV5vm8diExMTsW3bNlOuBgB4e3tDLpcjMTHRrK2fnx8AwM/PDwaDAcnJyVbbEBERPQx+vZ6NFw6mWgw2xjdxsfAKoKG7AgLy27f3VWJlTy8GG0RUrdgccPz0009QqVQYPny4aZuDgwNat26N/fv3S9ru378fHTp0AAC0bt0aSqVS0ubOnTuIiooytSEiIqrpDEYRM0+lW9znrBDwRnNXi/uWdvXEtvAcHBjki90DfFHHlTV7iah6semuJYoiVq5ciSeeeAKurtIb4muvvYYJEyagXbt26NChA77//nvExcXh+eefBwB4eHjg2WefxcyZM+Hr62taFrdZs2bo0aNHub8hIiKiqui3GC1uFqn6DQDdaqnwWjNXhLgp0NBdgSvpetO+JmoF2vsqcSUNCC2h5gYRUVVlU8Bx+PBhXL16Fd98843ZvieeeAIpKSmYO3cu4uPj0bRpU6xfvx7BwcGmNrNmzYJcLsfzzz8PrVaLbt264euvv2YNDiIieijcztRj6gmNZNszoc74XxdPyba+QSpcuZAfcDjKgZU9vVgXg4iqPZsCjm7dukGj0VjdP378eIwfP97qfpVKhblz52Lu3Lml7iAREVF1lGcQMedsBo4l5OJIXJ7Z/glh5lOoprdxh0wQcCfLgNeauaKRuuTCfkREVR0nghIREVWAj06n43//Wi7q91ZzV7TwMg8mPBxk+DTco6K7RkRkVww4iIiIylGmzojX/kzFlhtai/vb+yoxs727nXtFRFR5WKKUiIioHE07kWY12ACA/7TNnzZFRPSw4AgHERFROYnS6LD2SrbFfV4qGd5o7orugY527hURUeViwEFERFROvjiXAWORmn7+TjIcG+oHT5WMK04R0UOJAQcR2U1anhHpeUbsuq1FRp6Ip0Od4evE5bGpZojLNuDX6zmSbS29lFjZywtejvycE9HDiwEHEdnF1hs5GH8wBXnG+9s238jBnoG+UMjMv/W9manHJ6fTkWMQ8XozV3TwV9mxt0Sl9+3FTOgKfb4buMtxYLAv8zWI6KHHpHEiqnBavYjJRzWSYAMAzibr4LMiFhuuSue8i6KIFw+kYP21HPwWo8Xj25Ow/qrlefFkG71RxNmkPKRozStdP6h9d7R4+5gGv1yr/N9Rps6I04l5yNGLJTcuB3qjiMXnMxC8JhZfnJMugftyU1cGG0RE4AgHEdnBL9ezkZxrtLr/1T9T4eUow7cXs5ClM2JQiBNOJupM+w0iMOmoBn2DHOGpqtnfk6Tfm3YW4Cy3OPJTIENnhItCKPaB9rtLmfj2YhZisw1Iz8t/AHdTCtj6mA/a+Dg8cF9FUcSCa0r8FJt873xZ0BmB0Q2dH/jYpfFPig7H4nOh1YuYdy4DaXkiGror8Gs/b9Rxrdj/zS06n4mP/0o32+6pEjAm1L7XgYioqmLAQUQVShRFLLuQVWwbnREYvivZ9PNhC1WZs/Ui6v10F2dH+KOuW827dYmiiHcj07DsYhaMYn5gMKahM95v5w43pUzSburxNCy/lAV3BwHPhLrgvbZucFZIA7HoNB3ePpZmdp4MnYjZZzPwcx/vB+7zL9dz8FOstHjdx6fTMbSuExwVFf/Nvt4o4pO/0rHwvHlxvSvpeozak4wDg/zgIK+Yvmj1Iuafy7C4b1ILN8nvjYjoYca7IRE9sPQ8I5K1BoiidBpLjl7EpKManEvRWXll6T1/IMXsPDXBxms5WHohy7TCUYZOxLKLWaiz+i5+vZ6N2CwDXj2cCs8fY/HtpSyIANLyRCz5NxMzT5l/w34iwTxoK3Dobv5owIPQG0V8ZuGb/TvZBsw+mw5jKX9H++9o8eSuJDRddxdvHkmFvuhST0Vk6YwYvivZYrBR4EKqHr/F5Fjd/yBStAa03xSPLAvXsYWXEi81damQ8xIRVUc172tCIrKrLTdy8MaRVKTniWjhpcScCA909FchLtuA4buS8G+q3uw1g0IcodWL2H0nt9TnO5Okwz+perTwUpbc2A4Kgp/Cy51GJuTii3OZ+CdZB29HGTr4OeCNFq4ItjK9J0tnxIxI89GIAs8fSC22Dz9cysJrzVwlIz830q3namTrRRyLz0XP2mWvB/F7jBbXMiyfY8H5TGy+kYOdA3zhV8IqZDqjiJmn0vDVv/dHwVZezkZLLyXGN3W1+BpRFPHfU+k4eLfkz8+pxDwMr1++U5tEUcSLB1NxO0v6/rvVUmFyC1d09FfZZYSHiKi6YMBBRGV2JC4X4w+mmFbmOZ+iw9CdSfj9MV98cS7DYrCx9TEfdKuVv+LUwVgthuxMNmtT2NC6Tth8Q/ot9e7b2goLOGKzDPj2YiZclDJMDHOBSzHTYtZEZ+GDU+nI0BnRJUCFHoEq9K7tiKE7k5F975vvO9kGnEvR4dtL+Q/UTdQKvBLminpucjRSK+HvJMOifzKRpLWe41ISvQi89mcqfn3UxzR96Eam+bUvbNdtbZkCjtRcI6Ye12DDteJHDq5nGPDR6XT8r4un9X4bRTy5OxkHYs0Dh5WXsyUBx65bWqy8nIXfb1qv4G3JRY0em6/nYNdtLQyiiGy9CH8nOYbWc0KXgNKtfKbJNeJWlgEHYrXYX6TPzgoB/+uithpUEhE9zHhnJHrI6Y0iZAJMyccGo4hsg2g2/zw+24C3j2kQn2PAO63c0SnAARMOpUqWAQWAXAPQ949Ei+fqF6RC14D7ycrdAx3xTKgzVkdbXt3oqy5qjAl1wSen0zGv0Fz53be1+L+WbmV5u8XKfwC+PypzOjEPay3kOuQaRFzP0OONIxrTFKg9d3Kx504u3j9pPs2osEsaPSYd1QAAZALMisSV1ZG4PCw8n4Gprd0BANfTpQHHS01d8O3F+6MIR+OtT7myRqsXMWB7Ii5YCCTruslxo8iIx4Zr2fiwvTu8LdSgEEUR31zMshhsAMC5FB3+TdGhmZcSS//NLHYEKNhVjm39fZCaJ0IhAB03J5j2HYjNtRLQZGHfID80tzFwPZech4Hbk5CuM/+FuSsFbHzUm8EGEZEVzOEgqqL0RhFfns/AywdTsPxiJrL1Zf8G3JrIhFy0+yUePitiMeOEBu9FpqHe2ruos/ouBmxPxNmkPFNfntiVhN9vanEyUYcXD6RgyjGN2ZSS4vzQwxM/9vQ2q7T8ySMe8HcyvxWpHQQMrusEABjbWDolJjIhDxlFI51y8MdNrWRUZvstLQ4Xmbbz9YVM1Fkdi4hfEx44WLD2+l8e9UY9t+KnIrkrBagdpNdyVXS2aYrX9SIP/08XWTnqfIrO6jU8EKvFhEMp+PZipiRf5qcr2RaDjVrOMqzu5Q2HIr/GXAPw+VnzpOrUXCOe2JWMd4sJIoD8wPLQ3dwS230a7oEgVwVaeCkR6qEw64cleUZg8T+WE74tnuNMhsVgw1kh4MBgP4T7sU4MEZE1DDiIqqivL2Tiv6fSsf5aDt45noY+vyciz1A+X4eLooiVl7MwaEcSYjINMIrA0gtZWPJvpmn51CNxeRi8MwmL/8lA03VxkgfxTL2In6/anoz7flt3DKvnDCcL89rVKhnW9/VGy0LfNDfzVOD7Hl5wvTfKEuyqQEP3+98eG0SYai1oillu15qDsbnosiUBdVbHosfWBByMzUW23oiF580fQAftyL8GsVkGJGsN+O/JNLN6IuUp0FmGnoEqbH3MB682kyYe13WT4+wIf1waFYBrY2rh+DB/FB6IupVpwN/JOqTlGZFS6LooZfmJzI097l9DowictJBYfiNDjyd3J2Pd1RxMOZ6GH6Pujz7tvG15OtPgECc091Ji5wBfuBT5HX97MQvP7U8xBcwGo4in9yabTUkCgBebSN/vlXQ93jySipI+9Y8G3Z8appAJaKS2bdRi0/UcxGdbD5r1RhF3sw1YdzUbO2+Zv3cBwLfdPFHfnSMbRETF4V2SqIpaeVk6zehCqh77YrV4rI5TmY/5T4oOpxLzsOh8htk34Jak54n4TwlThADA11GGk0/447FtibiksZwkXpxW3g44NMSv2DYd/R1wpdA0of87qsHNTAMUMmBOhBpjG9m2KlCGzohx+5OhuRdYnU3WYcjOpGJf85+T6Zh1JgND6jrZHGwEucixZ6AvXJUCHv3D8jQkSya3dINMEFDHVYHPwtUYWtcJyy9moZ67AhPDXKEuVIckwFmOR4Mc8UehvIbNN3IwtK70MxLiqoBcJiDC3wFRaff7cTQ+D72K5HH8HpMjmSY3+ZgGzzV2Rq4BZqM9ANDA2YgprfOnt7XxccCl0QHo+GuCZPRr840cdPJ3wMthrth2S2txOlcrbyUi/Bzw3aX7076sTbUrrKlaAVWRZW/D1Ar8Y8PKaDojsOOWFuMam392Lml0GLYzCXezLf/Cm3spMbOdO/oGlT3xnojoYcERDqIq6JJGh8tp5g+oV4tZeagkv17PRpctCZh0VGNTsFEaTzZwglolw899vNErUCWZ6jOukTMa2/iNc3Ei/KWF6q5lGKAXAa0BeP9kms2jP2ujs03BRmlk60WsvWJbJW2ZAPzY0wsBznK4KmXYNcAXX3ZW4+c+Xrj7bCBSnwvE99094VdoKlnf2ips7OuN8UW+5Q/3U+Gb7l6Y0cZdEmwUGBQiDS4Wns9Ez9+kOTQF07M6+Emv4b+FHsoNRhEXUnUW8x1OJ+nwW0yOKREeyB+Jufl0LfzURgufQjkabkoZFnZSmx2jYEWpE1ZyR56o51TiilYvNXXB+23dJdtebWa+kpW1woNLu3piRhtp7s/JRPP+xGUbMHSH9WDj2VBn/DnEj8EGEZGNOMJBVAWts/Jge7uElYes0RttG6koq9EN8h/w6ropsKmfD4D8qTlZOhHNymk1qY7+1ufIp+eJuKjRoZV38dWzk7QGfPmP9boNBeRC/rQtW4xs4ITdt7VIzb3/gqmt3NDe935fXJUysxGYJ+o7Y1BdJ1xL18PDQYYA5+Iftq0ZEOIIz0hBcv6iXX/kXqDRoMjUn7ic/MAzW2/EEzuTcdxK7Y7/nEwzS0LvG+QIdwcZ4i2s/tonyBEftHPHB6fvf+b+Ts4PbmIsfIb71lbh5aauFvcVUDsIeL+tO1QyAScT83AoNhcDQxwxxkJw0au2I1b09MIv17KxNUYLhQAMr++EkfWdcDhOGlCtjs7GJY0Oz4S64LnGLjgWn4uRu5ORYSFfo8ArYZaX6yUiIssYcBBVMZc1Oiz51/JDcWmStAv746bW6muXd/dEbRc5zibp4OEgQBAETDxsXvehtbcS/2nnjrH7UiTFzl5u6oKWFh70y7saeD03ORp5KCyO/ADAbze02HQtB6fuqNBek4ZXmrmiVqGHeK1exJg9KTZdwxU9vdDQQ4GPTqdjWzHLsEaNCoC/sxwGo2ianuPlKDMt+1sSpUx44NEfN6UMbzV3kzzcF1XQn6JBzZkkHb76NxMpuUarwQYAHCsyKqEQgAklPHRPbOaKT8+km6Zn3c4yIElrQEyR0bUdj/sg4l4w6edofdB9Ugs3eNzLBl/XxxsGowi5zHqtiyF1nTDk3tQyURRNixW08XGAAGlQdipRh1OJGlxM1eGnK9nFBhvfdvMstyCaiOhhwYCDqIr55mKW1TyBsgYc3xeaF18gxFWOt1u5YcS9omgFIwhX0izPfR9Wzwm9azvi35EByNQZ4aqUIUNnRB07LQUqCAIWd1Zj+K5kZFqo7nx/2Vw5jqRm4nyKzjTaAgAzIjWILDJ9JkytQKZexM3M+9c13NcBA4IdIQgCfurtDaMo4sUDqfi1SC2QJmoF/O89wMtlAuQyYGi9sufXPIgnGzgXG3C09ckPCC2NopS0ApQlb7VwRZhn8Q/dKrmAME+laWQDAM4m6cxGMQoHpmqVDEoZzJZaDnKR4/Xm0gCnuGCjqMIro3k4yNDIQyHJZSmw7KL5v5MCShmwvxTL6BIR0X0MOIiqmNNJ1r9pvp1Z+oDjdqYeh4ok+/45xPqDk7WRie73viVXq2SmXAJLOQUVqYO/CvsH+2LmqeJHHgBgX2wu4rINCHCWIzXXiBVFkvC71crPmXCQC4hO02He3xlwVgh4t4275AFVJgj4rocnHr/miJWXs3A4Lg9qBwGfhntUyHssi0BnGTxV0mlVBfrWVpmKAarkArxVMiSXYWWvAo5y4DULeROWtPKWBhw7b2uRVih/xlEOyZLIMkGAn6Mcd4qsHPV7fx8oShFglCTcz8FiwFHUoBBHNHRX4Gq6Hk+HujDYICIqIwYcRFWIURRxqZjVjBK1RvyTokMzT4VZPQtr1l/LkUwfae2tLPbBSSET0MxTIVkGt3OAA1p5V42HrVAPJX7q7Y0bGXq03hhfbNvj8XkYWs8Jf8blSmpeBLnIsbKnl+lBPNRDiWXdvKweRyYIeLKBM55s4AytXoQIWFzit7IIgoDmnkocjjMPVqe1kSZZBzg/WMAxsoEzvCwU8rOkva+DZLW1b4uMIAS7mn+O5RZi2DouZctvsWZCmCt+vpptNpJSmEIAlnXzhLOCa6sQET0o3kmJqpCYDANyCmUre6lkqF1kGkyXLQkYvisZfyXmYe2VbKQW8/AoiiJ+LpKA/pSVFXwKe7HJ/W+wG3sosLKnl80Bjr3UdVOgfgnF8Z47kIL55zLwn5PSaUODQhzLPDrjqBCqVLBRwN/CdKno0QGS5HUAkryW4rTyVmJKK+mKTv5OMkxv7W7lFeYGhziZ1eUoLMTVvC+WihGWZvqULZp7KbGki6ekhklRjTwUDDaIiMoJRziIKkBanhEXUnWmufO2MIoiJh3VSLY19VTAYITZFJN9sbnYF5u/9Gl9NzmODvWHESJWXs5G1L0Vd9r5OuBEQp4kybpgtZ6SvNDEBY3UCsRmGTAwxLHKPng939gF/zlV/OpbH1nIbbA1qbs6aeGlxMZr0jwTXwvLzBa3GtaiTmrIZfnBXCd/B+iM+UUSIxPzUMtZhrW9vRFYitEGtUqGMQ2d8a2FHCIACLEwfS+9DEsWl8XIBs5o66PEk7uTLS4T/WgdLnlLRFReGHAQlbPoNB36/J5omqs+LkiJV3x0aFpCku3yi1mmWgUFwtRKZOrFYlcQupZhwM9Xs/HdpSycv1dX4dfrOdj6mA9eOJAiaftoHUdJzYTidAmo+g/lzzZywayzGZL6ECWRCUCnavDeSmtkA2d8diYdufeenae3drPYrrjBmY7+DpIq3So5sGOAD66m6xHoLIdLcUMCVkxq6YZfrudIKp8X6BNk/nsY3dAZawoV/BsYXHEP/g09lDg0xA8vH0zF9kKVxJUyWFxul4iIyqZqfm1JVI19cCpdkhi74rYSfX5PNAUDloiiiGUXzZfCbeqpxAAbHrgmHdVIjq/JE9FtayJiixQuK6iXUVOoVflF5hzlgLNCwNKunujlXXwy8LOhzqblVWuSWs5y/NzbGwODHfF2S1e82cJyYndxydeWFgyQCQJCPZRlCjYAoLaLHDse90F4oald9d3k+KKjBx61UDjvlTBXU1AkF4D32to+hass3JQyrO3jjZTnAvFBO3cMDM6v4dGoHIpVEhFRPptGOOLi4vDBBx9g9+7dyMzMRN26dfHFF1+gS5cuAPIflj7//HOsWLECGo0G7dq1w7x589C0aVPTMTQaDaZOnYodO3YAAB577DHMmTMHarW6/N8VUSW5kaHHHxZWT8rSi3jjz1TsHugLpYUHvsiEPItVxB8NUpVYfdlWnfwd8HgFfltcWUY2cMbgECfoRRGuShma6/MwWPAxm54GACPqO+GzKrS6VHnrWdsRPWsX/zse2cAJy61McSpIoi9vjdRK7BroC02uscTcmRZeSuwZ6IvDd3PRo7ZjiSOD5UUmCJjU0vKoEBERPZgSAw6NRoN+/fohIiIC69evh7e3N2JiYuDr62tqs2jRIixZsgRLlixBaGgo5syZg2HDhuHkyZNwc8u/gY8fPx63b9/Gxo0bAQBvvvkmJkyYgHXr1lXQWyMqHyUVGCts9WXLFcIB4GyyDr4rYhHh54Axoc6mytOiKGK+qYbEfdv6+yDoXo2LSS1csfB8yRWyrYnwc8D6vt7lurRoVeKoEADkvzdHOfDcvarR51N0kAuAgyz/m/3yLkZYHT3i64DnGzvjhyjpZ9WnmKJ75cXWRP3WPg5oXYr8JyIiqtpK/L/vl19+iYCAACxbtsy0rW7duqa/i6KIpUuXYtKkSRgyZAgAYOnSpQgNDcXGjRvx/PPPIyoqCnv27MGOHTsQHh4OAFiwYAH69++P6OhohIaGlvPbIio9URSx6XoODsTm4tE6jng0yBFj96dg/x0tHg92wvLunsU+sIuiiM1FisNZcjwhD8cT8hDiqkD3QBW2xmix87Y0d2Ntby9JnsGMNu7I0os4m5SHk4nWp2ZZs7iLGq5lnBJTnbVg3QQzgiBgQSdPvNvGHS02xEF7b2Dt40dq7sgPERFVrhKfQP744w+0a9cOzz//PBo2bIguXbrgm2++gSjmz1GPiYlBfHw8evXqZXqNk5MTOnXqhBMnTgAAIiMj4erqig4dOpjaREREwMXFxdSGqDyJoogUrQFG0XIycVy2AXGFVn4yiiJeOpSKFw+mYlV0Np7dl4KevyVg5y0t8ozA5hs5ZisAFXVRo8eV9JKLiRUYsjMJi89n4N0T0iVbI/wc0K/ICjkquYC5EWrsHuiHaVaSga1p4aVEqAcfvEnK10mOPQP98FZzV3zX3ROjG1ROlXQiIqr5ShzhuHHjBr777ju8+uqrmDRpEs6fP49p06YBAF5++WXEx+cX3io8xarg57t37wIAEhIS4O3tLVnHXxAE+Pj4ICEhweq5o6OjS/+OqFRq4jW+mSPgg8sOOJ8hR6izEfPDchHgmB945BqBt/5V4XRafl5Ea3cDFjfLxW8JCmy8Jp3CcaFIAb6V/yShnSgdiSjsiytKAPcf7Bs4GzEhWAe1UsSMSyok68xHRywt6fpW7TRcvaKxep6hLkBSHSW2J8gRm1vyqEVrp+wa+XsuzsP2fstKBeAZNQADcOVK2Y/D620fvM72w2ttP7zW9lVR17uk2UolBhxGoxFt2rTBzJkzAQCtWrXCtWvXsHz5crz88svl00srONWqYtXE6Wx3sw2YsCUBSdr81Zmis2V44R8XvN/WHa28ldh9W4vTaffzJc6my3FAH4CNCVkAzJO2C0s2qhAaGmxx36nEPPwSlyjZ9lYbL4xt5ILo6GgceaIWPjydjrVXrOd4AEAHPwf0b127xPf5RSPgCwDX0vWYEZmGyxodOgeo8E4rN0w6qsGB2PuB0bg2gQj1fXjmw9fEz3VVxuttH7zO9sNrbT+81vZVmde7xIDD398fjRs3lmxr1KgRbt++bdoPAImJiahTp46pTWJiIvz8/AAAfn5+SE5OhiiKplEOURSRlJRkakP0oH64lIXJxzRm2xO1RrxlYcWiAp/+lW6ax16c21kG01TCOX9n4NDdXAwJcULv2o4YtjNJ0ra+mxxP1r+/BG2AsxxLu3riiXpOeHJ3stVzlHbZ2vruCqzr4y3ZtqiTGiN2JyMmQ4+JYa5mlaaJiIiI7KnE+RgRERG4UmSs/cqVK6bgIiQkBP7+/ti/f79pv1arxbFjx0w5G+Hh4cjMzERkZKSpTWRkJLKysiR5HURldTA212KwYQtbgg0gf2nbfn8k4fuoLMw6k4EjcXmYeiIN7TbFI0MnzRX5+BEPOFmosNY3yBE7HvdBU7XlWH9w3QdftjbETYGTT/jj1jOB+JCJwERERFTJSgw4Xn31VZw8eRLz5s3DtWvXsHnzZnzzzTcYP348gPxcjIkTJ2LRokXYunUrLly4gFdffRUuLi4YMWIEAKBx48bo06cPJk+ejMjISERGRmLy5Mno168fh9KozHINImadSUeXLQkYUmSE4UF8390Tw+s5wVNloV5GYh7ePpZm4VX3OSsE9LVQ0KxAhL8KR4eaj+wFOMngbWMVcFuoKqimAhEREVFplDilqm3btlizZg0++ugjzJ07F0FBQXj33XdNAQcAvPXWW8jJycGUKVNMhf82bdpkqsEBAMuXL8fUqVMxfPhwAED//v0xZ86cCnhLVNNl6Yw4EpeH90+m4XKa9VWh3m3jhifqOWF1dDaOx+fhnxQdMvWWV60qoBCAR+s44ol706EmHUnFj8XU1rCkR6CqxAJqgiBgeD0n/HL9/spXb7di0TEiIiKqeWyqgtWvXz/069fP6n5BEDBjxgzMmDHDahu1Wo1vvvmm9D0kKmT/HS1ePJiKlFxjse3GNHTG5JZuUMoEfNA+f1pRWp4RH59Ox547WtzIsDyPqr2vg6RexRP1nUsdcNhazXtKazdsu6lFjkFEU7UCT4eWLn+DiIiIqDpg2V2qNqI0Ojy1N7nEnIvnGztjQSdPs+0eDjLM66gGAPwWk4Nn96WYtekeqJL83K2WCsu7e2L8wVSb+vh4sKPNid9N1EpcHBWAf1N1aO/rwClQREREVCMx4KBq48t/MksMNnwdZfi/liVPTRoY7Ijprd2w+J9MZN2bZhXiKsfEMFeztiPqOyPCzwF9fk9EXI4RCgFo6KHAqAbOGNvIGXJBgFolQ45etJgoXhy1SobOAaqSGxIRERFVUww4qFpI0hqw8ZrlqU2+jjKcHeGPq+l6NPRQwFlRcjE8QRAwvY07prdxh8Eo4pJGj8ZqBRQyywFDkKsCZ0YEIEqjQ0MPhWTaVYHSBhtEREREDwMGHFQtrIjKRq6V0Y0JYa5wUcrQ0rts9SbkMgHNvJQltnNSCGjtw5oWRERERKXBgIOqPJ1RxHeXMiXbVHLA11GOTv4OeKO5+TQoIiIiIqoaGHBQlXcgNhex2fdXpXJWCLgwMgBqVclTp4iIiIiocvGJjaq86CK1NgaHODLYICIiIqom+NRGVV6KVlpzo64bB+aIiIiIqgsGHFTlJRfJFvd25MeWiIiIqLrgkxtVeclFRji8OZ2KiIiIqNrgkxtVecm5RQIOjnAQERERVRt8cqMqr2gOh5ejvJJ6QkRERESlxYCDHsima9no90ciXv8zFbFZVirzPSCzEQ5OqSIiIiKqNrjcD5XZmaQ8jD+UCqMInEjIw85bWizt6glnhYB67grUcn7wkQhRFM1yOLwYcBARERFVGww4qMw+/SsdRvH+z4laI0bsTgYAKGXAxr4+6B6oeqBzpOWJMBQ6h6tCgKNCeKBjEhEREZH98KtissmRuFw8uy8ZH5xKgybXiNln07HnTq7V9joj8PYxDfSFI5IyMBvdYMI4ERERUbXCEQ4q0a5bWozem2wazVh4PtOm111J12PKcQ2+6KiGTCjbqARrcBARERFVb3x6o2Jl6IyYcDgFJQ1U1HG1nK/xQ1Q2ph1PgyjmH+Cn6Cz0+yMRU45pkKEzWnxNYazBQURERFS98emtBovJ0GPacQ2++DsDujJObToQm4vU3JJfu3egLwYEO1rc9+2lLKy/loMojQ6v/anBiYQ8fHspC2P3pSDPUPyx72YzYZyIiIioOuOUqhoqLtuAXr8lmpaUPZuch5U9vSBYmdr06/VsLDiXiWBXOeZ2VJtWmPo7SVfiufrWVsHPSY7VvbxwO8uAy2l6vHo4FfE594OFN4+kwkslQ+HwYn9sLgZuT8LaPl7wtlJb42i8NE8k1IMfWSIiIqLqhF8X11BTj2sk9St+i9Hil+s5FtteSdPhpYOpOJeiw+83tfjkr3TTvr+T84o9Ty1nGT56xAMAIAgC6rgq0Lu2Izb09YasUGyTazAfrQCAyMQ8jNmbAq3efKRDFEUcuisNOLrVerBVr4iIiIjIvhhw1DA/X8mG+oc72BqjNdv3xp8anE0yDyBmn81A4ef9NdHZiMnQ49O/0rG7yEpUYxo6o4lagQHBjjg7wh/nngxAU0+l2TFbejvgiXpONvX5REIeFp7PMNselaZHQqFREheFgLa+DjYdk4iIiIiqBs5PqUFStAZMPqqxuj/HIOLNIxocGOxrWjUqQ2fE1hjzkY82v8SbJYo7yQV82VkNhcy2Fadea+aKX67lwJbskeWXsjCphZukxsa2m9KgqZO/A5Q2npuIiIiIqgaOcNQgkYl5yCkhCftcig6/FRr9OB6fhyIrzwKAxVWpWngpbQ42AKCNjwNmd/CAtZe4K+/vSNIa8cv1bNPPoihi7ZVsSfu+QZaT0omIiIio6mLAUc1l6oym4npnLSR4uyoEPBokzXsYtz8Fg7Yn4rd4OSITis/RKOyJ+rZNkSrs5TBX7B3oa7a9d20Vnm3kItlWOBA6k6RDdJre9LNSBgwvw/mJiIiIqHIx4KjGFp3PQP2f7qLRz3E4GpeLv5PNA44JYS74oL2H2fbDcXn4KFqFuX+b505YEugsw3NFAgRbtfFxwJWnAhDgdP/j9kJjF4xqIA0gIhPyYLxXr2NfrDR3pG+Qo9WVrIiIiIio6mIORxW1/WYOZp5Kh4eDgIWdPNHMS5qYfS1djw9OpUMEkJJrxOt/pppNp3q9mSumt3GHQgB8HGVI0pZcaM+aLzqqJfkVpeXjKMe2x33xR0wOWvk4oGuAA4xi/rSqdF1+v1NyjYhO06OxWonjRZbD7cfpVERERETVEgOOKsYoitAaRLz+5/1lbd84kop9g/yg1YvYdVsLDwcZ/ozLlSRjX8uQJmKo5MDM9u6mJOtW3krsLbLilK0ODPJFa58HXx2qvrsCb7RwM/0sF4BwPwfsKdSvDr8mYF6Eh2QbAET4c3UqIiIiouqoxClVs2bNglqtlvxp1KiRab8oipg1axaaNGmCgIAADBgwABcvXpQcQ6PR4OWXX0ZwcDCCg4Px8ssvQ6PRlPubqe5+vpKN+j/dReCqu5IaGn8l6RCTocez+5Ixdn8KhuxMKnEqVDNPpWRFp9be5kvXFtW3tgr13MynLZVHsGFNhL95XY13jqdJfvZSydCIBf+IiIiIqiWbcjhCQ0MRFRVl+nP06FHTvkWLFmHJkiWYPXs29u3bB19fXwwbNgwZGfcfiMePH49z585h48aN2LhxI86dO4cJEyaU/7upxuKzDXjzSCo0eZZXmfr0jHlNjOK0KhJgNFGXHHC80swV8zuqJdv61anYqUyPB5d8/A5+DlYrpBMRERFR1WZTwKFQKODv72/64+PjAyB/dGPp0qWYNGkShgwZgrCwMCxduhSZmZnYuHEjACAqKgp79uzBwoULER4ejvDwcCxYsAA7d+5EdHR0xb2zaub7qCzkFZNisf6q5Srh1rT2lo5K9KytgkuhHAwfR+mvvqO/A3oFqtAjUIXxTfKTw/2dZJje2g0VKcxTicWd1cW2eaqhc4X2gYiIiIgqjk0Bx40bN9CkSRO0bNkSL7zwAm7cuAEAiImJQXx8PHr16mVq6+TkhE6dOuHEiRMAgMjISLi6uqJDhw6mNhEREXBxcTG1edhdS9fjq38zy/WYRUc4fBzlWNxZjSZqBfrWVuHgYD/8p2Eu6rrJ0TnAAd9284QgCBAEAfM6qnFjTC38PSIAbSpwOlWBZxu54I3mrmbbI/wc8FNvLwyuy+VwiYiIiKqrEifGt2/fHl999RVCQ0ORlJSEuXPn4tFHH8Xx48cRHx8PAPD1ldZZ8PX1xd27dwEACQkJ8Pb2lkyJEQQBPj4+SEhIKPbcNXEExCgCeUZAJQMEARBFYPw5FTJ05bfkq1IQ4ZAcg+hU6fYWAFY1z/97dmwqBgcAgwPyp77l3NWgMq92fycBa5SOSNEJECBibtM8dPfOBnI1qAkfg5r4Wa7KeL3ti9fbPnid7YfX2n54re2roq53aGhosftLDDj69u0r+bl9+/Zo3bo1fvrpJzzyyCMP1rsSlNT56mbTtWxMOZ6G5FwjQj0UWNhJjWBXOc4diS/X83TwVyGscVCJ7aKjo6vMNQ4FcKCuHjtvadHa2wGP+NWcVamq0nV+GPB62xevt33wOtsPr7X98FrbV2Ve71IX/nN1dUWTJk1w7do1+Pv7AwASExMlbRITE+Hn5wcA8PPzQ3JyMkTxfjK0KIpISkoytXkYRGl0eOVwqmn1qeg0PSYf1eBCql7SLthVjm+7ecLXUQY/J9t+PZHD/PBEPSeoHQSEeijwySPmhf6qg2BXBV5q6lqjgg0iIiKih12pAw6tVovo6Gj4+/sjJCQE/v7+2L9/v2T/sWPHTDkb4eHhyMzMRGRkpKlNZGQksrKyJHkdNd20E2lmSeGX0/TYc0cr2dYrUIUnGzgj+qlauDy6FhZ1Ukv2CwCcCyV/966tQiO1Et/38MKNpwNx8gn/Cl3GloiIiIioNEqcUvX+++/jscceQ1BQkCmHIzs7G0899RQEQcDEiRMxf/58hIaGomHDhpg3bx5cXFwwYsQIAEDjxo3Rp08fTJ48GQsXLgQATJ48Gf369XtohtE0uUYciLW8pO3qy9mSn5t6SpO9xzZyxv7YXGy+kb9K1ecdPBDkIsenZ9LhpZLhs/DqOZpBRERERA+HEgOO2NhYjB8/HsnJyfDx8UH79u2xe/duBAcHAwDeeust5OTkYMqUKdBoNGjXrh02bdoEN7f7y6kuX74cU6dOxfDhwwEA/fv3x5w5cyroLRVPFEX8GZeHTJ0RfYIcJcXxKsqNDL3VfTkGad2NogGHIAj4rrsnJoS5wF0pQzOv/P0DQrhyExERERFVfSUGHN9//32x+wVBwIwZMzBjxgyrbdRqNb755pvS964CzPk7A7PO5K/M1MxTgbV9vBHsWrFVrK8XE3AUFeZp3he5TEBHCxW5iYiIiIiqulLncFR3yy9mmf7+b6oe4w+kwmAUkWewXOG7PFzPMNjULsBJBh/H8lsel4iIiIiosj1UAUeK1oBErTRzOzIxD94rYtHo57tYeyXbyisfTHFTqgobXp8VtYmIiIioZnloAo5svRFnk3VW92vyREw/oUG23mi1TVldTy854BAAvNTUpdzPTURERERUmSo2eaGSJGsN+OBUOjR5Rrzd0g0JOUa8dCgFaXnFT5tKyxMRpdGjTTkvK/tPqjTQqecmN5tm1cpbibpuNfLXQUREREQPsRr3hGswihi5Oxmnk/If8k8m5MFBLpQYbBS4kKort4BDFEUs+TcTqbnSc3erpcL1DOn0rbAiq1MREREREdUENS7g+PpilinYAIC4nNJNkbqYavuKUiX59K8MzDuXIdnW0d8BjdTmwUVTC6tTERERERFVdzXqKVeTa8Tss+kPdIyLGut5HqWRaxCx+F9psOEkF/BZuAfuZJmvWsURDiIiIiKqiWpUwLHk30yk2zh1CgD+09Ydl9N0WHc1x7TtaFwejKIImWBeEDBDZ4SDTIBKbr1Y4KnEPMw4oUFUmh65ReKKFT290MbHAXIhz+x1TS2MehARERERVXc1ZpUqURSxJjqr5Ib3TGvthrdbuWFxZ08oC12FHIOI7lsTEVVkpOOXa9lo+nMcAlfFYuVly+fR6kU8vTcZJxN1ZoFPBz8HPFrHEQBQ310hOaebUkAt5xrzqyAiIiIiMqkxT7lX0/WIzbYtX+P77p6Y0cYdAOAgF/BokKNk//kUHZ7emwKjmB80iKKI9yLTkKkXYRCBt49pEGOhtsbG69mIt5Iz0tLr/giGq1KGV8JcTT+/19YdgoURFSIiIiKi6q7aT6kSRRFH4vPwxd/SfIkgFzluF8mVEABcHh0AXydpNe/PO3jgVGKeJFi4kq7HzUwD6ropcCfLIEk+1xmBOX9nYEkXT8lxvr1ofYSloYf0Un/8iAdGNXCGSg6EenA6FRERERHVTNV+hGPJv5kYuD0J+2NzJdtfbOKCVt7SB/kXmriYBRsAUMdVgd/7+5htH7ErGXezDbioMR/NWHclG7GFApobGXr8XUxhwVAP89iuuZeSwQYRERER1WjVPuBYfsnyqEK3Wios6eKJ1t5KBLvKMSvcA/MiPKweJ9RDifFNpJW+r6Tr8eKBFFxINQ8k9CLwfdT9c++5rS22n0VHOIiIiIiIHgbV+ik41yDiRob5ErP+TjK09lZCLhNwYLCfzcezVAvjaHwenBWW8yu+vZiJl5q4wN9Zjt3FBBzuDgKCXMxHVoiIiIiIaroqPcLxzYVMvHUkFR+fTsPNTPNpTTcsJG4DwJP1nSGXlT4J29rStHvv5FrcnpYnYuoJDe5kGcymdA2pez8RfUorN4vL7BIRERER1XRVeoRj6ok009/XXsnGn0P84OV4f6TgarrlgOOphs5lOl9TK8X3iqvsseWGFsfiE5BXaHGqEFc5fuzhhesZBogi0IDTqYiIiIjoIVWlRzgKi8024t3INMk2SwHHK2EuaOZVtkRsT5UMzSxMqyqsgbscbXykx08oshTu/7V0gyAIqO+uYLBBRERERA+1ahNwAMC6qzm4XWhq1fV0af7GS01c8HkH9QOd44uOxb/+6VAXLO7sCStpHfBzkmFMaNlGWIiIiIiIappqFXCIAOb9nQG9MX+SU9ERjh6Bqgc+R4S/CvOtBB0CgJH1ndDcS4n2vg4W23Twc4CyDPkjREREREQ1UZUPOIJdpas7/Xg5G2P2JiNTZ8SpxDzJvvKavhToYvmyDK/vhCDX/HMUrfFRwFogQkRERET0MKrSAYeLQsDHj5jXzth1OxcRvyYgS38/nbuWswyh7uUTcNRyNl/C1sNBwGfh9/vCgIOIiIiIqGRVOuBo6a3EI1Ye4G9nSfM3RpZxKVxLaluomTGqgTP8ClUpb+1j3i+5ALS2EogQERERET2MqnTA0dpbiUAXOQKdS+7m6DIuhWuJt0oGJ7k0eBndQHr8Rh4KeKqKtGnoDBdllb6kRERERER2VaWfjgtGEcY1dim23cQwF6s1NMpCEAQ82cDJ9HPf2iqzpXAVMgGfhavhrBDgKAfeaO6KhZ3U5dYHIiIiIqKaoEoXiegakL/q1Nst3VDPTYHPzqTjRoZ0KtWbzV3xkYU8jwc1N0KN9r4OMBiBJ+o7QbBQKfyphs4YGOIIAYArRzaIiIiIiMxU6YAj8F4uhUImYGQDZzxZ3wmTj2rw4+VsAEB7XyXeb+teIedWyQWMbVT8yAoAuDHQICIiIiKyqkoHHEUJgoAFndQYEOKEZK0Rjwc7wkHOmhdERERERFVVqb+enz9/PtRqNaZMmWLaJooiZs2ahSZNmiAgIAADBgzAxYsXJa/TaDR4+eWXERwcjODgYLz88svQaDSl7rAgCOgb5IjRDZ3h7sDRBSIiIiKiqqxUT+wnT57Ejz/+iGbNmkm2L1q0CEuWLMHs2bOxb98++Pr6YtiwYcjIyDC1GT9+PM6dO4eNGzdi48aNOHfuHCZMmFA+74KIiIiIiKokmwOOtLQ0vPTSS/jf//4HtVpt2i6KIpYuXYpJkyZhyJAhCAsLw9KlS5GZmYmNGzcCAKKiorBnzx4sXLgQ4eHhCA8Px4IFC7Bz505ER0eX+5siIiIiIqKqweaAoyCg6Natm2R7TEwM4uPj0atXL9M2JycndOrUCSdOnAAAREZGwtXVFR06dDC1iYiIgIuLi6kNERERERHVPDYlja9YsQLXrl3DN998Y7YvPj4eAODr6yvZ7uvri7t37wIAEhIS4O3tLVlaVhAE+Pj4ICEhwep5OfpR8XiN7YPX2b54ve2L19s+eJ3th9fafnit7auirndoaGix+0sMOKKjo/HRRx9hx44dUCrLr7ieLUrqPD2Y6OhoXmM74HW2L15v++L1tg9eZ/vhtbYfXmv7qszrXeKUqsjISCQnJyMiIgLe3t7w9vbGkSNHsHz5cnh7e8PLywsAkJiYKHldYmIi/Pz8AAB+fn5ITk6GKIqm/aIoIikpydSGiIiIiIhqnhIDjgEDBuDo0aM4fPiw6U+bNm0wfPhwHD58GA0bNoS/vz/2799veo1Wq8WxY8dMORvh4eHIzMxEZGSkqU1kZCSysrIkeR1kX/xWwT54ne2L19u+eL3tg9fZfnit7YfX2r4q83qXOKVKrVZLVqUCAGdnZ3h6eiIsLAwAMHHiRMyfPx+hoaFo2LAh5s2bBxcXF4wYMQIA0LhxY/Tp0weTJ0/GwoULAQCTJ09Gv379+GEjIiIiIqrByqXS+FtvvYWcnBxMmTIFGo0G7dq1w6ZNm+Dm5mZqs3z5ckydOhXDhw8HAPTv3x9z5swpj9MTEREREVEVJWg0GrHkZkRERERERKVXqkrjREREREREpcGAg6gSzJo1Cx07dqzsbhARUQl4vyZ6cHYPOCZOnIhRo0bZ+7QPnbNnz8LLywv9+vWr7K48FPi5to/Y2Fi89dZbCAsLg6+vL5o2bYo333wTd+7csen1MTExUKvVOHPmTAX3tPrjZ9p+eL+2L3627YP3a/upDp9pjnDUUKtWrcKLL76IixcvIioq6oGPp9PpyqFXRGV348YN9OzZExcvXsTSpUvx119/YdmyZbh06RJ69eqFmJiYyu4iUZnwfk01De/XVFSlBhx//fUXhg0bhvr166NOnTp47LHHJLU6gPxleX/88UeMGzcOgYGBaNWqFdatW1dJPa4ecnJysGHDBjz33HMYPHgwVq1aZdpX8I3Bhg0b8Nhjj8Hf3x+PPPII9u3bZ2pz+PBhqNVq7Nq1C7169YKvry/27t1bGW+lWrL0TQOH5B/clClTIJPJsHnzZnTv3h116tRBt27dsHnzZshkMkyZMgVAflHRxYsXo23btvDz80NYWBg+/PBDAECrVq0AAD179oRarcaAAQMq7f1UJ7xXVxzerysX79cVg/frylNV79eVGnBkZGRg1KhR2L59O/bu3YsWLVrgySefREpKiqTdnDlz8Pjjj+PPP//EE088gddffx23bt2qpF5XfVu2bEGdOnXQrFkzjBo1Cj///LPZN14zZ87EhAkTcPjwYfTo0QNjxoxBbGyspM0HH3yA999/HydPnkT79u3t+RaIJFJTU7Fnzx6MHz8ezs7Okn3Ozs548cUXsXv3bmg0Gnz00UeYO3cuJk+ejOPHj+PHH39E7dq1AcD0oPbLL78gKioKq1evtvt7qY54r644vF9TTcP7deWqqvfrSg04unfvjtGjR6Nx48Zo1KgR5syZA0dHR+zevVvSbtSoURg1ahTq16+P9957DwqFAkePHq2kXld9q1atwujRowEAXbp0gZOTE7Zt2yZp88ILL2DYsGFo1KgRZs+ejdq1a+P777+XtJk2bRp69eqFunXrwsfHx279Jyrq6tWrEEURjRo1sri/cePGEEUR//77L7766it88MEHePbZZ1G/fn2Eh4dj/PjxAABvb28AgJeXF/z9/eHp6Wm391Cd8V5dcXi/ppqG9+vKVVXv15UacCQmJmLSpElo164dgoODERQUhMTERNy+fVvSrlmzZqa/KxQKeHt7IzEx0d7drRauXbuG48ePm6q8C4KAkSNHSobpAeCRRx4x/V0mk6Fdu3a4dOmSpE2bNm0qvsNE5cjR0RG5ubno3r17ZXelRuG9umLwfk0PM96vK0ZVvV+XS6Xxspo4cSISEhLw2WefITg4GCqVCoMHD0ZeXp6knVKplPwsCAJEkfUKLVm5ciUMBgOaN29u2lZwrYp+2Eri4uJSrn17WMhkMrPPp16vr6Te1Az169eHIAiIiorCoEGDzPZHRUVBEIRK6NnDgffqisH7deXj/br88X5duarq/bpSRziOHz+Ol19+Gf369UPTpk3h6uqK+Pj4yuxStabX67F27VrMnDkThw8fNv35888/0axZM6xZs8bU9tSpU6a/i6KIv/76C40bN66Mbtc4Pj4+iIuLk2w7f/58JfWmZvDy8kLv3r3x3XffITs7W7IvOzsby5cvR9++fdGoUSOoVCocPHjQ4nEcHBwAAAaDocL7XJPwXl3+eL+uGni/Ln+8X1euqnq/rtSAo0GDBli/fj0uXbqEv/76Cy+88ILpA0alt3PnTiQnJ2PcuHEICwuT/Bk+fDjWrFljil6///57bNmyBdHR0Zg+fTpu3bqFF154oZLfQc3QrVs3nDt3DqtWrcK1a9ewaNEiHD9+vLK7Ve3NnTsXer0eQ4cOxcGDB3H79m0cPnwYw4YNgyiKmDNnDtzc3PDKK6/gww8/xOrVq3H9+nWcPn0a3333HQDA19cXTk5O2Lt3LxISEpCWllbJ76p64L26/PF+XTXwfl0xeL+uPFX1fm33gMNoNEIulwMA/ve//yErKws9evTACy+8gGeeeQbBwcH27lKNsWrVKnTt2hVeXl5m+4YOHYqbN2/iwIEDAPJXPVmyZAm6dOmCvXv3YvXq1aaVIaj0Cn+ue/fujWnTpuGTTz5Bjx49cPPmTVMSHJVdvXr1sH//fjRp0gSvvPIKWrdujZdeegmNGjXCvn37ULduXQD5n+1JkyZh7ty5CA8Px9ixY00r+igUCsyePRurVq1CkyZNMGbMmEp8R1Ub79UVi/frysP7dcXj/dq+qsP9WtBoNHadYDts2DDUq1cP8+fPt+dp6Z6YmBi0atUK+/fvZ5JhOeLnmmoafqYrH+/XFYOfbappqsNn2m4jHMnJyfjjjz9w5MgR9OjRw16nJapQ/FxTTcPPNNVU/GxTTVOdPtN2W6Xqueeew7Vr1/Dmm29aXLWAqDri55pqGn6mqabiZ5tqmur0mbb7lCoiIiIiInp4VOoqVUREREREVLMx4CAiIiIiogpTIQHH/Pnz0bNnT9SpUwcNGjTAqFGjcOHCBUkbURQxa9YsNGnSBAEBARgwYAAuXrwoaTNv3jz069cPgYGBUKvVFs918OBBPProowgKCkKjRo0wc+ZMVgklIrJBedyrY2Ji8Prrr6NVq1YICAhAq1at8OGHHyInJ0dynFu3bmHUqFEIDAxE/fr1MXXqVLPKt0REZJk979fTpk1Djx494O/vjxYtWpRL/ysk4Pjzzz/x4osvYufOndi6dSsUCgWGDh2K1NRUU5tFixZhyZIlmD17Nvbt2wdfX18MGzYMGRkZpja5ubkYOHAgJk6caPE858+fx5NPPokePXrg0KFD+P7777F9+3Z88MEHFfG2iIhqlPK4V0dHR8NgMGD+/Pk4fvw45syZg59//hnTp083HcNgMGDUqFHIzMzEtm3b8N1332Hr1q1477337P6eiYiqI3vdr4H8uh5PPfUURo8eXW79t0vSeGZmJoKDg7FmzRr0798foiiiSZMmeOmll/DOO+8AAHJychAaGoqPP/4Yzz//vOT1W7Zswbhx46DRaCTbP/roI+zevRuHDx82bdu+fTuef/55REdHw83NraLfGhFRjfGg9+oCy5cvx6efforr168DAHbv3o2RI0fi/PnzCAoKAgCsW7cOb775JqKjo+Hu7m6fN0hEVENU1P26sMWLF+Obb77B+fPnH7i/dsnhyMzMhNFoNE2LiomJQXx8PHr16mVq4+TkhE6dOuHEiRM2Hzc3NxeOjo6SbU5OTtBqtTh79mx5dJ2I6KFRXvfqjIwMyTTYyMhING7c2BRsAPkVnnNzc3mvJiIqg4q6X1cUuwQc06dPR4sWLRAeHg4AiI+PBwD4+vpK2vn6+iIhIcHm4/bu3RunTp3CunXroNfrERsbi9mzZ0vOQUREtimPe/XNmzexePFivPjii6ZtCQkJZsfw9vaGXC4v1T2fiIjyVdT9uqJUeMDx7rvv4vjx41i1ahXkcnm5HrtXr174+OOPMWXKFPj7+6N9+/Z49NFHAQAyGRfgIiKyVXncqxMSEjBixAj07NkTr732Wjn3kIiIgOp5v67Qp/IZM2bgl19+wdatW1G3bl3Tdn9/fwBAYmKipH1iYiL8/PxKdY7XX38dMTEx+Oeff3D16lU8/vjjACA5HxERWVce9+r4+HgMGjQITZs2xbJlyyAIgmmfn5+f2TGSk5NhMBhKfc8nInqYVfT9uqJUWMAxbdo00wVp1KiRZF9ISAj8/f2xf/9+0zatVotjx46hQ4cOpT6XIAioVasWnJycsHHjRgQFBaFVq1YP/B6IiGq68rhXx8XFYeDAgWjUqBG+++47KBQKyXHCw8MRFRWFO3fumLbt378fKpUKrVu3rpg3RkRUw9jjfl1RKuQs77zzDtatW4fVq1dDrVab5pW5uLjA1dUVgiBg4sSJmD9/PkJDQ9GwYUPMmzcPLi4uGDFihOk4t27dQmpqKm7evAkAOHfuHACgfv36cHV1BQB8+eWX6N27N2QyGX777TcsXLgQP/zwQ7lP3yIiqmnK41599+5dDBw4EAEBAZg1axaSk5NNx/fx8YFcLkevXr3QtGlTvPLKK/jkk0+QmpqK//73vxg7dixXqCIisoG97tcAcO3aNWRmZuLu3bvQ6XSm5+8mTZrAwcGhTP2vkGVxrWW7T5s2DTNmzACQX5zk888/x48//giNRoN27dph3rx5CAsLM7WfOHEi1q5da3ac3377DV27dgUADBo0CH///Tfy8vLQvHlzTJs2DX379i3vt0REVOOUx716zZo1Vuf//v333wgJCQGQ/wXSO++8g0OHDsHR0RFPPvkkPv74Y6hUqvJ/Y0RENYw979cDBgzAkSNHim1TWnapw0FERERERA8nLuVEREREREQVhgEHERERERFVGAYcRERERERUYRhwEBERERFRhWHAQUREREREFYYBBxERERERVRgGHEREREREVGEYcBARUYnWrFkDtVpt+uPv748mTZrgiSeewNdff42MjIwyHffSpUuYNWsWYmJiyrnHRERUVSgquwNERFR9TJ8+HfXq1YNOp0NCQgL+/PNPzJgxA0uWLMHatWvRvHnzUh0vKioKs2fPRpcuXcpcwZaIiKo2BhxERGSz3r1745FHHjH9/H//9384ePAgRo8ejaeeegqRkZFwcnKqxB4SEVFVwylVRET0QLp3744pU6bg1q1bWL9+PQDgn3/+wauvvorWrVvD398f9evXxwsvvIBbt26ZXrdmzRqMGzcOADBo0CDTdK01a9aY2vz111948sknERwcjICAADz22GM4dOiQfd8gERE9EAYcRET0wEaNGgUA2LdvHwBg//79uHLlCkaPHo05c+Zg7Nix2LNnDwYOHIjs7GwAQOfOnTFhwgQAwNtvv41ly5Zh2bJl6Ny5MwDgzz//RP/+/ZGamoopU6bggw8+QG5uLp544gkcPny4Et4lERGVhaDRaMTK7gQREVVta9aswWuvvYbdu3dLplQVFhwcjLp16+LQoUPIzs6Gs7OzZP+JEyfQr18/LFu2zBSgbNmyBePGjcNvv/2Grl27mtqKoojw8HAEBgZi8+bNEAQBAJCXl4du3brB3d0du3btqqB3S0RE5YkjHEREVC5cXV2RmZkJAJJgIzMzEykpKWjYsCE8PDxw9uzZEo91/vx5REdHY8SIEUhJSUFycjKSk5ORkZGBHj164NSpU6aREiIiqtqYNE5EROUiMzMTPj4+AACNRoMPPvgAW7ZsQWpqqqRdenp6ice6evUqAOCNN97AG2+8YbFNSkqK2SgKERFVPQw4iIjogd25cwfp6emoX78+AOC5557DiRMn8Nprr6Fly5Zwc3ODIAh44YUXYDQaSzxeQZsPPvgArVu3ttimILghIqKqjQEHERE9sHXr1gEAevXqBY1GgwMHDmD69OmYPn26qY1Wq4VGo7HpePXq1QOQP02rR48e5d1dIiKyI+ZwEBHRAzl48CDmzp2LkJAQjBw5EjJZ/v9aRFG6JslXX31lNrrh4uICAGaBSOvWrVG/fn0sWbLEYhXzpKSkcnwHRERUkTjCQURENtu7dy+uXbsGvV6PxMREHDp0CPv370edOnWwdu1aODo6wtHREV26dMGXX34JnU6HOnXq4NixYzh69Ci8vLwkx2vZsiXkcjkWLFiAtLQ0ODk5oV27dqhbty4WL16MESNGICIiAk8//TRq166Nu3fv4siRIxBFEb///nslXQUiIioNBhxERGSzzz//HADg4OAAT09PhIWFYdasWXj66afh5uZmard8+XJMnz4dP/zwA/R6PTp16oStW7diyJAhkuP5+flh0aJFmD9/Pt566y0YDAYsWbIEdevWRefOnbF7927MnTsX3333HTIyMuDn54e2bdti7Nixdn3fRERUdqzDQUREREREFYY5HEREREREVGEYcBARERERUYVhwEFERERERBWGAQcREREREVUYBhxERERERFRhGHAQEREREVGFYcBBREREREQVhgEHERERERFVGAYcRERERERUYRhwEBERERFRhfl/iZoIb5AHfmMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt #导入matplotlib.pyplot\n",
    "plt.style.use('fivethirtyeight') #设定绘图风格\n",
    "df_app[\"Activation\"].plot(figsize=(12,4),legend=True) #绘制激活数\n",
    "plt.title('App Activation Count') #图题\n",
    "plt.show() #绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建一个函数，将数据集转化为时间序列格式\n",
    "def sliding_windows(data, seq_length):\n",
    "    x = []\n",
    "    y = []\n",
    "\n",
    "    for i in range(len(data)-seq_length):\n",
    "        _x = data[i:(i+seq_length)]\n",
    "        _y = data[i+seq_length]\n",
    "        x.append(_x)\n",
    "        y.append(_y)\n",
    "\n",
    "    return np.array(x),np.array(y)\n",
    "\n",
    "# 设定窗口大小\n",
    "seq_length = 4\n",
    "\n",
    "x_train, y_train = sliding_windows(Train, seq_length)\n",
    "# 使用滑动窗口为测试数据创建特征和标签\n",
    "x_test, y_test = sliding_windows(Test, seq_length)\n",
    "\n",
    "# 将数据转化为torch张量\n",
    "testX = Variable(torch.Tensor(np.array(x_test)))\n",
    "testY = Variable(torch.Tensor(np.array(y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 以不同颜色为训练集和测试集绘图\n",
    "df_app[\"Activation\"][:'2020-09-30'].plot(figsize=(12,4),legend=True) #训练集\n",
    "df_app[\"Activation\"]['2020-10-01':].plot(figsize=(12,4),legend=True) #测试集\n",
    "plt.legend(['Training set (Before October 2020)','Test set (2020 October and beyond)']) #图例\n",
    "plt.title('App Activation Count') #图题\n",
    "plt.show() #绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置模型参数\n",
    "input_size = 1\n",
    "hidden_size = 64\n",
    "num_layers = 1\n",
    "output_size = 1\n",
    "\n",
    "class RNN(nn.Module):\n",
    "\n",
    "    def __init__(self, input_size, hidden_size, num_layers, output_size):\n",
    "        super(RNN, self).__init__()\n",
    "        \n",
    "        self.hidden_size = hidden_size\n",
    "        \n",
    "        # RNN层\n",
    "        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)\n",
    "        \n",
    "        # 全连接层，用于输出\n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # 初始化隐状态\n",
    "        h0 = Variable(torch.zeros(num_layers, x.size(0), self.hidden_size))\n",
    "        \n",
    "        # 前向传播RNN\n",
    "        out, _ = self.rnn(x, h0)\n",
    "        \n",
    "        # 解码RNN的最后一个隐藏层的输出\n",
    "        out = self.fc(out[:, -1, :])\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, loss: 0.35201\n",
      "Epoch: 10, loss: 0.04601\n",
      "Epoch: 20, loss: 0.00509\n",
      "Epoch: 30, loss: 0.00080\n",
      "Epoch: 40, loss: 0.00214\n",
      "Epoch: 50, loss: 0.00084\n",
      "Epoch: 60, loss: 0.00082\n",
      "Epoch: 70, loss: 0.00061\n",
      "Epoch: 80, loss: 0.00057\n",
      "Epoch: 90, loss: 0.00055\n"
     ]
    }
   ],
   "source": [
    "# 创建模型\n",
    "rnn = RNN(input_size, hidden_size, num_layers, output_size)\n",
    "\n",
    "# 定义损失函数和优化器\n",
    "criterion = torch.nn.MSELoss()    # 均方误差\n",
    "optimizer = torch.optim.Adam(rnn.parameters(), lr=0.01)   # Adam优化器\n",
    "\n",
    "# 将数据转化为torch张量\n",
    "trainX = Variable(torch.Tensor(np.array(x_train)))\n",
    "trainY = Variable(torch.Tensor(np.array(y_train)))\n",
    "\n",
    "# 训练模型\n",
    "num_epochs = 100\n",
    "for epoch in range(num_epochs):\n",
    "    outputs = rnn(trainX)\n",
    "    optimizer.zero_grad()\n",
    "    \n",
    "    # 计算损失\n",
    "    loss = criterion(outputs, trainY)\n",
    "    loss.backward()\n",
    "    \n",
    "    optimizer.step()\n",
    "    if epoch % 10 == 0:\n",
    "        print(\"Epoch: %d, loss: %1.5f\" % (epoch, loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date: 2020-10-05 00:00:00, Actual Activation: 923.0, Predicted Activation: 884.0242309570312\n",
      "Date: 2020-10-06 00:00:00, Actual Activation: 919.0000000000001, Predicted Activation: 892.1919555664062\n",
      "Date: 2020-10-07 00:00:00, Actual Activation: 915.0, Predicted Activation: 896.2152099609375\n",
      "Date: 2020-10-08 00:00:00, Actual Activation: 910.0000000000001, Predicted Activation: 894.8662719726562\n",
      "Date: 2020-10-09 00:00:00, Actual Activation: 905.0000000000001, Predicted Activation: 893.1639404296875\n",
      "Date: 2020-10-10 00:00:00, Actual Activation: 901.0, Predicted Activation: 889.68994140625\n",
      "Date: 2020-10-11 00:00:00, Actual Activation: 913.0000000000001, Predicted Activation: 886.35791015625\n",
      "Date: 2020-10-12 00:00:00, Actual Activation: 900.0, Predicted Activation: 887.63427734375\n",
      "Date: 2020-10-13 00:00:00, Actual Activation: 888.0000000000001, Predicted Activation: 884.4244995117188\n",
      "Date: 2020-10-14 00:00:00, Actual Activation: 883.0, Predicted Activation: 879.5723266601562\n",
      "Date: 2020-10-15 00:00:00, Actual Activation: 861.0, Predicted Activation: 876.6394653320312\n",
      "Date: 2020-10-16 00:00:00, Actual Activation: 844.0, Predicted Activation: 865.3352661132812\n",
      "Date: 2020-10-17 00:00:00, Actual Activation: 837.0, Predicted Activation: 853.5160522460938\n",
      "Date: 2020-10-18 00:00:00, Actual Activation: 841.0, Predicted Activation: 844.4507446289062\n",
      "Date: 2020-10-19 00:00:00, Actual Activation: 821.0, Predicted Activation: 837.6363525390625\n",
      "Date: 2020-10-20 00:00:00, Actual Activation: 843.0, Predicted Activation: 827.8084716796875\n",
      "Date: 2020-10-21 00:00:00, Actual Activation: 857.0, Predicted Activation: 829.9515380859375\n",
      "Date: 2020-10-22 00:00:00, Actual Activation: 861.0, Predicted Activation: 836.236328125\n",
      "Date: 2020-10-23 00:00:00, Actual Activation: 858.0, Predicted Activation: 838.9547729492188\n",
      "Date: 2020-10-24 00:00:00, Actual Activation: 832.0, Predicted Activation: 845.241943359375\n",
      "Date: 2020-10-25 00:00:00, Actual Activation: 811.0, Predicted Activation: 840.2474975585938\n",
      "Date: 2020-10-26 00:00:00, Actual Activation: 807.0, Predicted Activation: 829.5674438476562\n",
      "Date: 2020-10-27 00:00:00, Actual Activation: 803.0, Predicted Activation: 820.1813354492188\n",
      "Date: 2020-10-28 00:00:00, Actual Activation: 821.0, Predicted Activation: 809.6572265625\n",
      "Date: 2020-10-29 00:00:00, Actual Activation: 838.0, Predicted Activation: 809.337646484375\n",
      "Date: 2020-10-30 00:00:00, Actual Activation: 824.0, Predicted Activation: 816.549560546875\n",
      "Date: 2020-10-31 00:00:00, Actual Activation: 853.0, Predicted Activation: 817.1880493164062\n",
      "Date: 2020-11-01 00:00:00, Actual Activation: 860.0, Predicted Activation: 829.7672729492188\n",
      "Date: 2020-11-02 00:00:00, Actual Activation: 866.0, Predicted Activation: 838.7720336914062\n",
      "Date: 2020-11-03 00:00:00, Actual Activation: 864.0, Predicted Activation: 843.0267333984375\n",
      "Date: 2020-11-04 00:00:00, Actual Activation: 838.0, Predicted Activation: 850.3660888671875\n",
      "Date: 2020-11-05 00:00:00, Actual Activation: 815.0, Predicted Activation: 844.4197998046875\n",
      "Date: 2020-11-06 00:00:00, Actual Activation: 836.0, Predicted Activation: 833.728271484375\n",
      "Date: 2020-11-07 00:00:00, Actual Activation: 835.0, Predicted Activation: 831.875732421875\n",
      "Date: 2020-11-08 00:00:00, Actual Activation: 823.0, Predicted Activation: 826.77490234375\n",
      "Date: 2020-11-09 00:00:00, Actual Activation: 800.0, Predicted Activation: 821.0575561523438\n",
      "Date: 2020-11-10 00:00:00, Actual Activation: 818.0, Predicted Activation: 816.0709838867188\n",
      "Date: 2020-11-11 00:00:00, Actual Activation: 820.0, Predicted Activation: 815.360595703125\n",
      "Date: 2020-11-12 00:00:00, Actual Activation: 843.0, Predicted Activation: 813.4452514648438\n",
      "Date: 2020-11-13 00:00:00, Actual Activation: 857.0, Predicted Activation: 818.5724487304688\n",
      "Date: 2020-11-14 00:00:00, Actual Activation: 848.0, Predicted Activation: 831.2929077148438\n",
      "Date: 2020-11-15 00:00:00, Actual Activation: 868.0, Predicted Activation: 834.8007202148438\n",
      "Date: 2020-11-16 00:00:00, Actual Activation: 875.0, Predicted Activation: 845.9241943359375\n",
      "Date: 2020-11-17 00:00:00, Actual Activation: 842.0, Predicted Activation: 853.1670532226562\n",
      "Date: 2020-11-18 00:00:00, Actual Activation: 835.0, Predicted Activation: 845.4395751953125\n",
      "Date: 2020-11-19 00:00:00, Actual Activation: 855.0, Predicted Activation: 842.4212646484375\n",
      "Date: 2020-11-20 00:00:00, Actual Activation: 884.0, Predicted Activation: 844.0415649414062\n",
      "Date: 2020-11-21 00:00:00, Actual Activation: 890.0000000000001, Predicted Activation: 848.7324829101562\n",
      "Date: 2020-11-22 00:00:00, Actual Activation: 898.0, Predicted Activation: 857.0264282226562\n",
      "Date: 2020-11-23 00:00:00, Actual Activation: 880.0, Predicted Activation: 868.3079223632812\n",
      "Date: 2020-11-24 00:00:00, Actual Activation: 872.0, Predicted Activation: 870.7332153320312\n",
      "Date: 2020-11-25 00:00:00, Actual Activation: 858.0, Predicted Activation: 867.4928588867188\n",
      "Date: 2020-11-26 00:00:00, Actual Activation: 873.0, Predicted Activation: 861.1181640625\n",
      "Date: 2020-11-27 00:00:00, Actual Activation: 884.0, Predicted Activation: 858.6063842773438\n",
      "Date: 2020-11-28 00:00:00, Actual Activation: 912.0, Predicted Activation: 861.0447387695312\n",
      "Date: 2020-11-29 00:00:00, Actual Activation: 915.0, Predicted Activation: 870.529296875\n",
      "Date: 2020-11-30 00:00:00, Actual Activation: 923.0, Predicted Activation: 880.4934692382812\n",
      "Date: 2020-12-01 00:00:00, Actual Activation: 930.0000000000001, Predicted Activation: 888.9790649414062\n",
      "Date: 2020-12-02 00:00:00, Actual Activation: 925.0000000000001, Predicted Activation: 897.75634765625\n",
      "Date: 2020-12-03 00:00:00, Actual Activation: 941.0, Predicted Activation: 898.9815063476562\n",
      "Date: 2020-12-04 00:00:00, Actual Activation: 949.0, Predicted Activation: 905.0243530273438\n",
      "Date: 2020-12-05 00:00:00, Actual Activation: 890.0000000000001, Predicted Activation: 910.4224243164062\n",
      "Date: 2020-12-06 00:00:00, Actual Activation: 898.0, Predicted Activation: 895.95751953125\n",
      "Date: 2020-12-07 00:00:00, Actual Activation: 891.0, Predicted Activation: 892.5490112304688\n",
      "Date: 2020-12-08 00:00:00, Actual Activation: 896.0000000000001, Predicted Activation: 885.8046264648438\n",
      "Date: 2020-12-09 00:00:00, Actual Activation: 908.0000000000001, Predicted Activation: 876.4219360351562\n",
      "Date: 2020-12-10 00:00:00, Actual Activation: 908.0000000000001, Predicted Activation: 881.348876953125\n",
      "Date: 2020-12-11 00:00:00, Actual Activation: 909.0, Predicted Activation: 882.705322265625\n",
      "Date: 2020-12-12 00:00:00, Actual Activation: 925.0000000000001, Predicted Activation: 885.4598999023438\n",
      "Date: 2020-12-13 00:00:00, Actual Activation: 928.0000000000001, Predicted Activation: 892.4404296875\n",
      "Date: 2020-12-14 00:00:00, Actual Activation: 928.0000000000001, Predicted Activation: 896.0382690429688\n",
      "Date: 2020-12-15 00:00:00, Actual Activation: 908.0000000000001, Predicted Activation: 898.7053833007812\n",
      "Date: 2020-12-16 00:00:00, Actual Activation: 920.0, Predicted Activation: 896.1653442382812\n",
      "Date: 2020-12-17 00:00:00, Actual Activation: 926.0, Predicted Activation: 896.9140625\n",
      "Date: 2020-12-18 00:00:00, Actual Activation: 931.0000000000001, Predicted Activation: 898.0679931640625\n",
      "Date: 2020-12-19 00:00:00, Actual Activation: 931.0000000000001, Predicted Activation: 898.4423217773438\n",
      "Date: 2020-12-20 00:00:00, Actual Activation: 937.0, Predicted Activation: 902.1102294921875\n",
      "Date: 2020-12-21 00:00:00, Actual Activation: 921.0, Predicted Activation: 905.4835205078125\n",
      "Date: 2020-12-22 00:00:00, Actual Activation: 918.0, Predicted Activation: 902.7408447265625\n",
      "Date: 2020-12-23 00:00:00, Actual Activation: 926.0, Predicted Activation: 900.0712280273438\n",
      "Date: 2020-12-24 00:00:00, Actual Activation: 946.0, Predicted Activation: 900.9403686523438\n",
      "Date: 2020-12-25 00:00:00, Actual Activation: 937.0, Predicted Activation: 904.7733154296875\n",
      "Date: 2020-12-26 00:00:00, Actual Activation: 939.0000000000001, Predicted Activation: 905.857666015625\n",
      "Date: 2020-12-27 00:00:00, Actual Activation: 933.9999999999999, Predicted Activation: 908.8323364257812\n",
      "Date: 2020-12-28 00:00:00, Actual Activation: 933.9999999999999, Predicted Activation: 910.0449829101562\n",
      "Date: 2020-12-29 00:00:00, Actual Activation: 917.0, Predicted Activation: 907.9761962890625\n",
      "Date: 2020-12-30 00:00:00, Actual Activation: 911.0000000000001, Predicted Activation: 902.8703002929688\n",
      "Date: 2020-12-31 00:00:00, Actual Activation: 903.0, Predicted Activation: 897.576416015625\n",
      "Date: 2021-01-01 00:00:00, Actual Activation: 890.0000000000001, Predicted Activation: 892.1886596679688\n",
      "Date: 2021-01-02 00:00:00, Actual Activation: 914.0, Predicted Activation: 883.322509765625\n",
      "Date: 2021-01-03 00:00:00, Actual Activation: 910.0000000000001, Predicted Activation: 886.0281372070312\n",
      "Date: 2021-01-04 00:00:00, Actual Activation: 923.0, Predicted Activation: 885.7136840820312\n",
      "Date: 2021-01-05 00:00:00, Actual Activation: 935.0, Predicted Activation: 889.5135498046875\n",
      "Date: 2021-01-06 00:00:00, Actual Activation: 950.0, Predicted Activation: 898.9168701171875\n",
      "Date: 2021-01-07 00:00:00, Actual Activation: 956.0000000000001, Predicted Activation: 906.0433959960938\n",
      "Date: 2021-01-08 00:00:00, Actual Activation: 953.0000000000001, Predicted Activation: 913.8355712890625\n",
      "Date: 2021-01-09 00:00:00, Actual Activation: 960.0, Predicted Activation: 917.7619018554688\n",
      "Date: 2021-01-10 00:00:00, Actual Activation: 962.0000000000001, Predicted Activation: 922.4721069335938\n",
      "Date: 2021-01-11 00:00:00, Actual Activation: 965.0000000000001, Predicted Activation: 924.7225341796875\n",
      "Date: 2021-01-12 00:00:00, Actual Activation: 967.9999999999999, Predicted Activation: 926.1985473632812\n",
      "Date: 2021-01-13 00:00:00, Actual Activation: 967.0, Predicted Activation: 928.8524169921875\n",
      "Date: 2021-01-14 00:00:00, Actual Activation: 949.0, Predicted Activation: 929.6959228515625\n",
      "Date: 2021-01-15 00:00:00, Actual Activation: 952.0, Predicted Activation: 925.3722534179688\n",
      "Date: 2021-01-16 00:00:00, Actual Activation: 940.0, Predicted Activation: 923.8565063476562\n",
      "Date: 2021-01-17 00:00:00, Actual Activation: 923.0, Predicted Activation: 918.6678466796875\n",
      "Date: 2021-01-18 00:00:00, Actual Activation: 928.0000000000001, Predicted Activation: 909.4031372070312\n",
      "Date: 2021-01-19 00:00:00, Actual Activation: 916.0000000000001, Predicted Activation: 907.2088012695312\n",
      "Date: 2021-01-20 00:00:00, Actual Activation: 914.0, Predicted Activation: 900.447509765625\n",
      "Date: 2021-01-21 00:00:00, Actual Activation: 916.0000000000001, Predicted Activation: 895.6022338867188\n",
      "Date: 2021-01-22 00:00:00, Actual Activation: 925.0000000000001, Predicted Activation: 895.2348022460938\n",
      "Date: 2021-01-23 00:00:00, Actual Activation: 926.0, Predicted Activation: 895.7719116210938\n",
      "Date: 2021-01-24 00:00:00, Actual Activation: 920.0, Predicted Activation: 897.4120483398438\n",
      "Date: 2021-01-25 00:00:00, Actual Activation: 932.0, Predicted Activation: 897.3292846679688\n"
     ]
    }
   ],
   "source": [
    "# 使用训练好的模型进行预测\n",
    "rnn.eval() # 设置模型为评估模式\n",
    "test_outputs = rnn(testX)\n",
    "\n",
    "# 将预测结果逆归一化\n",
    "test_outputs = test_outputs.data.numpy()\n",
    "test_outputs = Scaler.inverse_transform(test_outputs) # 逆归一化\n",
    "\n",
    "# 真实测试标签逆归一化\n",
    "y_test_actual = Scaler.inverse_transform(y_test)\n",
    "\n",
    "# 输出预测和真实结果\n",
    "for i in range(len(y_test)):\n",
    "    print(f\"Date: {df_app['2020-10-01':].index[i+seq_length]}, Actual Activation: {y_test_actual[i][0]}, Predicted Activation: {test_outputs[i][0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vX79Gp9MxZcqUBPePC9yIu35LQjOh88hJjfsnub+ruJcUvV6fXFN8JxRBl06Jy0d6/vy5xe1xEV6meUvlypXDzc2NQ4cOERwczJkzZ4wPvFq1aiEIAgcPHjS+pcvD9+OOdefOHbtNaUnJeVuyZAkQ8zCO8yPJ+fvvv3n16pWZmc3a5xKndVrK50rKPnJ0Oh0rV64E4s3Slli1ahXfffcdDg4OxgdBnJk5pejYsSPjx49n1apV9OvXj+vXr3P69GmjpmnKpEmT0Gq1+Pv7U7hwYcm2QYMGWYyItJek3MPpAWv3spubG9HR0TabdOOuK+7+kmPtc5GTGveP6XdlqepOev2ubEURdOmU0qVLs23bNo4cOUKDBg0k2yIjI41mxrjgEoh5i6pWrRq7du1i5cqV6HQ66tSpA0DWrFkpXrw4hw8fNpoV5IKuYsWKnDt3juPHj6d4BYzz589z9uxZsmfPTuPGjS2O+e+//zh79iyrVq3im2++kWx7+PAh9+7dM3tLj3tAW/IXnj9/nuDgYDPzZUL7yNm5cydPnz4lX758ViMOT506xfXr19mxYwctW7akfPnyqFQqTp48SUhISKLmy7i3YXs1Fy8vL+rVq8fevXv577//2LBhAyD1I8Zx+/ZtihQpYibkDAYDJ0+eTJY5xX2ep06dMprMTYnzE1syfaVHKlasyO7du7l48aIk3cMapikZcqx9zpYoVKgQ7u7uXL16lYcPHyZqvkzKd1W6dGnOnz/PkSNHzPL44iK0XV1dKViwoM3HTE8owSjplA4dOqDValm0aBHXr1+XbJsxYwaPHz+mUaNGZuaMOOE1Y8YMtFqtMZk5btu///6Lv78/3t7eFChQQLJvnz590Gq1jBo1yuycEKPNHD58OFmuLy5go3fv3syZM8fi3y+//ALEmArl6PV6xo4dK/kx37lzh4ULF+Lg4ED79u3N9gkKCmLq1KmSdadPn2bTpk14eHjwySef2Dzvb7/91uq8v//+ewBjME3WrFlp27Ytz58/5/vvvzd7AIWHhxuj5gAyZ84MkKRgkLgggJUrV7Ju3TpcXV0tpk3kyZOH27dvS7QEURT56aefLAYbeXh4IAgCDx8+tHkuuXLlon79+jx69IhZs2ZJtl25coXFixfj6Ohok180PRCXsjJo0CAePXpktj0iIoITJ04YlytXrkzBggU5deoUW7ZskYxdtGiRTf45iBFcvXv3JiIigsGDB5uZlHU6nUQ7TMr9E5e/OmPGDEk+oCiKjBkzhrCwMDp37pyuKsjYg6LRpVPy5MnDlClTGDx4MHXr1qVVq1Z4enpy6tQpjh07Rq5cuZg+fbrZfnGC7sWLF1SvXh0XFxfjtjjn/5s3b2jatKnZvgULFuS3337jq6++omrVqjRo0IACBQqg1+t59OgRp06dIjIykvv377/TtYWEhLB+/XrUarXxB2aJMmXKULJkSS5evMjRo0clGlTx4sU5ffo0derUoV69esY8q6CgICZOnGjRH1O1alWWLVvGmTNnqFKlCo8fP2bTpk2IosisWbMS1bTu3buHv78/Hh4eZukDpnzyySdky5YNf39/o9b5888/c+XKFZYtW8axY8eoX78+Tk5OxsCWuXPn0rx5cwDq1q3L5s2bGThwIH5+fmTIkAF3d3f69OmTyCcLTZs2JVOmTCxatIjo6GiL/k+ICUP/3//+R61atfDz80Oj0XDq1CmuXbtGkyZNzBKzM2TIQKVKlTh16hQdO3akdOnSODg4UK1aNYvBFnHMmDGDJk2aMHHiRA4fPkzFihWNeXQRERHMnDkzxQIskptatWrx448/MmbMGMqXL0/Dhg3JmzcvERERPHjwgOPHj5MnTx6OHj0KxJhA58yZQ+vWrfnss88keXT+/v40aNCAffv22XTub7/9ljNnzrBnzx7KlStHkyZNcHNz49GjRxw6dIiBAwfSv39/IOb+mT17NuPHj+fKlStGU2RC6QyVKlVi8ODBzJgxg6pVq9KqVSvc3Nzw9/fn/PnzFCtWjNGjR7/bB5iGKBpdOuazzz5j8+bNVK1ale3btzNnzhwePHhAnz598Pf3txgRV6xYMaNDWW6arFatmvGNzFoeUrt27Th06BBdunThypUrLFy4kNWrV3P9+nUaNmzI77///s7XtWHDBoKDg2nYsGGi9fZ69OgBYJZq4OHhwa5duyhcuDArVqxgzZo15MuXj0WLFllMFoeYaM49e/bg5ubGokWL2LJlC+XKlWPjxo02JYvHBaF06NDBLMTbFAcHBzp37owoikZt1MPDgz179jB69GicnJxYvnw5Cxcu5L///qN9+/YS8123bt2MD6XffvuNiRMnMmfOnETnB+Do6Ei7du2MYeCWzJYQc2/NnTsXT09PVq9ezV9//UWuXLnYt2+fxBxuyoIFC2jWrBn//PMP06ZNMwqvhIhLa/niiy+4e/cuc+bMYceOHVSvXp0tW7bQvXt3m64rvfDNN9+we/dumjVrxpkzZ5g/fz7r16/n/v37dOjQwcxnW6VKFXbu3EndunXZv38/v//+OxEREfz99992RSprtVrWr1/PtGnTyJEjB2vXrmX+/PkEBARQv3596tataxxbt25dJk+eTObMmVm4cCETJ05k4sSJiZ5j9OjRLF26lCJFivDXX3/x22+/ERYWxtChQ9m9e/d7658DEAIDAy2XB1BQSIfcu3eP0qVLU716dbZv327TPkeOHKFFixZ07tyZefPmpfAMFRQU0huKRqegoKCg8EGjCDoFBQUFhQ+aNBV0wcHBjBgxghIlSuDl5UWjRo34999/jdutNS0dOnSocUxcJW7TP3k4voKCgoLCx0uaRl0OGDCAS5cuMW/ePHLlysXatWtp1aoVJ0+eJGfOnFy7dk0y/uzZs3Tq1Mks4q1OnTosWLDAuGyp3JDCh4GPj4/dZYVq1qyZbkoRKSgopD42a3THjh0z64FmyqtXr+yqphAeHs7WrVsZM2YMNWvWJH/+/IwcOZJ8+fIZix57enpK/nbs2IGvr69Zoq6jo6Nk3LsWyFVQUFBQ+HCwWdC1aNECf39/q9sPHTpk1h4kIXQ6HXq93ixM29nZWZJ0GUdISAgbN240hpubcuLECXx9fSlfvjwDBgywWnJHQUFBQeHjw2ZBZ61JYRxRUVFm7UcSImPGjFSqVIlp06bx+PFj9Ho9a9euJSAgwGKn3rjK+/LitA0aNGD+/Pls2bKFCRMmcObMGfz8/N65IK2CgoKCwodBgj66oKAgSbXq169fWywrExgYyPr16+2urr1gwQK++uorihUrhlqtpnTp0rRr145z586ZjV22bBmffPKJWcdp015SxYsXN1bT2L17N35+fhbPe+PGDbvmqaCgoKCQfkmsBmeCgu63334z1gYUBIGRI0da7S4siiJjx461a3L58uVjx44dhIaGEhwcjJeXF5999plZpfULFy5w9uxZm0rQ5MiRg5w5cyZYR+5dC5PeuHHjvS1umlSUa/44UK754+Bju+YEBV29evVwdXUFYsrDtGvXzqzCuyAIuLq6UrZs2SRXIXd1dcXV1ZXAwED279/P+PHjJduXLVuGj4+PsRJ/Qrx69YonT54Y+ycpKCgoKHzcJCjoKlWqRKVKlYCYZpt+fn4UK1Ys2U6+f/9+DAYDBQsW5M6dO/zwww8UKlTIWIEdICwsjL/++osBAwaY9YkKCQlh8uTJ+Pn54enpyf379xk/fjzZsmUzFshVUFBQUPi4sTmPbsSIEcl+8qCgIMaNG8fjx4/JlCkTfn5+jBo1StIKYuPGjYSGhkqEXxxqtZrLly+zZs0a3r59i6enJzVr1mTJkiVmPccUFBQUFD5O7CrqHBd0cvfuXQIDA80iMQVB4Ndff032SaYWOp2O0NDQRMcFBQW915W8k4JyzamDq6srGk3a1XH42Hw3oFzzx4DNv6j9+/fTo0cPQkNDyZgxo8V269Za0L8P6HQ6goODjQ0mE8LR0THBNi0fIso1pzyiKBIYGEjGjBnTVNgpfGAEBqI5fRpDgQIY8uVL69mkCTb/mkaNGkX27NlZsWIFxYsXT8k5pQmhoaE2CTkFhZRCEAQ8PDwICgrC3d09raej8AGgunIF11atUMXmJkfXqUNUr17wEWlzYEfC+O3bt/nyyy8/SCEXhyLkFNIa5R5USDYCA3Hp0sUo5AAcDh7EtXt3inXrhvD0aRpOLnWxWdAVKFCAkJCQlJyLgoKCgkJyoNfj8vnnqO/csbjZ5eZNtAsXpvKk0g6bBd3333/P4sWLuXv3bgpOR0FBQUHhXXH88Ucc9u9PcIz64sVUmk3aY7OP7sCBA2TKlInKlStTq1YtcuXKhVqtlowRBIFp06Yl+yQV0p5jx47Rtm1bbt26RZYsWdJ6OgoKClZw2LgRp5kzJet0lSoR+dVXuJoUxVfdu5fKM0s7bBZ0ca1zAPbt22dxjCLoUp9+/fqxevVqICavMEeOHDRq1IjRo0dbjIxNae7cucP06dPx9/fnxYsXZM+enXLlyvHVV19RuXLlVJ1Ls2bNKFasGD///HOqnldBIa1QXbiA81dfSdYZvLwIW74cHB2lY+/eBYMB7CjG/75is6B78+ZNSs5D4R2Iazyr0+m4du0aX3/9NW/fvmXRokWpOo+zZ8/SsmVLChUqxPTp0ylcuDChoaHs2bOH4cOHc+jQoVSdj4LCx4Tw6hWun36KEB5uXCdqtYStWIHo5QWiiOjmhhAUFDM+IgLh2TNEO4vxv498+KL8IyCu8WyuXLmoV68erVu35sCBA5IxK1eupHLlynh6elK+fHnmzp2LwWAwbv/111+pVq0aOXPmpGjRonzzzTd2deUWRZH+/fvj4+PD7t27adKkCfny5aNEiRIMHjyYLVu2GMdeunSJli1b4uXlRd68eenXr5+kS0a/fv3o2LGj5Pg//fQTVatWNRszb948ihYtio+PD/379ycsLMy4/dixY/zxxx94eHjg4eHBvY/IVKPwkREZiUvPnqhk3WXCp09HX7FizIIgmOXRqT6SmAslKzUR3C2Y/1Iyw+mtHcLFEnfv3mX//v2SMmrLli1j0qRJTJ06ldKlS3PlyhUGDhyIg4MDffr0AUClUvHTTz+RN29eHjx4wPDhwxk+fDi///67Tee9cOECV65c4Y8//jDz3QJGM2poaCht27alXLly7N+/nzdv3jBw4EC+/vprVqxYYde1njhxAk9PTzZv3syjR4/o2bMnvr6+DB48mMmTJ3Pr1i0KFixo7Hohb/GkoPAhILx4gUvXrmhOnZKsj/ziC6K7dZOsM+TNi/r8eeOy6s4d9CYvkB8qNgu6TJky2ZTj8/r163eakIL97Nu3j1y5cqHX64mIiABg4sSJxu0///wz48aNo2XLlgDkzZuXO3fusGjRIqOg69+/v3G8j48P48ePp0uXLsyfP9+mhrpxbZEKFSqU4Lj169cTFhbGggULjPVIZ86cSYsWLbh9+zb58+e3+bozZszIL7/8glqtpnDhwrRq1YpDhw4xePBg3N3dcXBwwMXFRelkofDBorp4EdfOnVE9fChZr6tWjYhJk8zGG2Qt0BSNTsbw4cPNBJ1er+f+/fvs2LEDX19fGjdunOwTVEicatWqMWvWLMLDw1m2bBl3796lb9++ALx8+ZKHDx/yv//9jyFDhhj30el0klqlhw4d4pdffuH69esEBQWh1+uJiori2bNnNjXUTawDfRzXrl2jePHikqLblStXRqVScfXqVbsEXeHChSXao5eXF6dPn7Z5fwWF9xnVzZtkaNoUQZbfrPf1JWzZMjCx6hi3KabLhLHWcBXg6dOnNGjQAF9f32SZlIJ9uLi4GAXE1KlTad68OVOnTmXkyJFGP9yMGTOsRj3ev3+fjh070r17d7777jsyZ87M+fPn+fzzz4mKirJpDgUKFADg+vXrlC5dOknXEfcipVKpzASnTqczG+8g+yELgmCzwFVQeN9xnDzZTMjpatcmbOlSxEyZLO6jaHTvgJeXF7169eLnn3+mXbt2yXHIdIMln1lERES6LnD87bff0r59e3r27EmOHDnIkSMHd+7coXPnzhbHnz17lqioKH766SejhrRr1y67zlmqVCmKFCnC7NmzadOmjZmfLjAwEA8PDwoXLszKlSsJDg42anWnTp3CYDBQuHBhIMaXdlGWzCpftgWtVoter7d7PwWFdE9oKA47dkhWRfbuTcRPP1nU5OL4WAVdskVduri4KFFt6YSaNWtSuHBhY07jyJEjmT17NnPnzuXGjRtcvnyZ1atXM2PGDCBGGzMYDPz222/cvXuX9evXM3/+fLvOKQgCc+fO5e7duzRp0oRdu3Zx584dLl26xKxZs2jVqhUA7du3x8XFhb59+3Lp0iWOHTvG//73P1q0aGHUSmvVqsWFCxdYsWIFt2/fZtasWZw8edLuzyFPnjycOXOGe/fu8erVK0mUqYLC+4zD7t0IsRHGAIacOYmYOjVBIQcgensjmryEqp4/h4+gtGOyCLrLly+zYMECo/lKIe2Ji2K8f/8+3bt359dff2Xt2rXUqFGDpk2bsmzZMnx8fAAoUaIEkydP5rfffqNKlSosX76cH3/80e5zli9fnoMHD1KwYEEGDx5MpUqV6NixI2fOnDEmbbu4uLBhwwaCg4OpX78+Xbp0oWLFipI+hvXr1+fbb79lwoQJ1KlTh/v379O7d2+75/PNN9+g1WqpUqUKBQoU4IEs9FpB4b0hOBjVjRsxCd6Aw4YNks3RrVvblvit0WDInVuy6mOokGJz49VSpUpZjLp8+/YtQUFBuLi48Oeff1K7du1kn2Rq8PbtW5tbo6R302VKoFxz6mHPvZjcfGwNOSH9X7Pq3Dky+PkhBAURXacO4fPnk7FUKQQT/3mIvz/6smVtOp5L69Y4+Psbl0P//BNds2bJPu/0hM0+uurVq5sJurj+Wfny5aNt27ZksuIAVVBQUFBIGs7ffWesZuJw8CCqJk0kQk6fLx/6MmVsPp6Zn85Kh4MPCZsF3bx581JyHgoKCgoKMlTXrqE5flyyTi0LIIlu2xbs6GNoJug+AtNlkqIuRVHk1atXAGTJkkVpFqmgoKCQAmiXLk10THTbtnYd82OMvLQrGOX27dv07NmTPHnyUKhQIQoVKkSePHno1auXsTKGgoKCgkIyEB6OQ2xnEmvoixXDULSoXYe1aLoURYRnz8CkIPSHhM2C7sqVK9SpU4cdO3ZQr149hgwZwpAhQ6hXrx7bt2+nXr16XLlyxa6TBwcHM2LECEqUKIGXlxeNGjXi33//NW7v16+fsSBv3F+DBg0kx4iMjGTYsGHkz5+fnDlz0qlTJx49emTXPBQUFBTSGw6bN6MyyeMVM2RAlFnPotu0sfu4ckGnvnkTly5dcCtcmIzlyqG6fDkp003X2Gy6HDt2LC4uLhw8eNCsTNOdO3do2rQp48aNY82aNTaffMCAAVy6dIl58+aRK1cu1q5dS6tWrTh58iQ5c+YE4lvQxKHVaiXHGDlyJDt27GDRokVkypSJ77//no4dO3Lo0CGLxYUVFBQU3gfkZsuonj0xZM+Oc2yRctHdnaiuXe0/sLs70e7uOJh0DHHYuRMA1ZMnOM6YQfjChUmed3rEZkF34sQJBgwYYLEWYb58+fj888+ZM2eOzScODw9n69atLF++nJo1awIxQmvXrl0sXryYUaNGAfEtaCzx9u1bVqxYwdy5c6lbty4ACxYsoGTJkhw8eJD69evbPB8FBQWF9ILq8mWzbgRRPXti8PXFUKQI6vPniW7VKqbPXBKIypVLIuhMkZ/3Q8Bm06Ver8dR1qHWFCcnJ7vKLel0OvR6vVmekrOzMydOnDAunzhxAl9fX8qXL8+AAQN48eKFcdu5c+eIjo6mXr16xnXe3t4ULlyYUx/gl6WgkGIEBqLZuhXt48dpPRMFQLt8uWRZV7MmhthawrpGjYgcNgzDO+T+RXh7W92mevAAITbY8EPBZo2udOnSLF++nG7duhl7i8URGBjI8uXLKWNHLkfGjBmpVKkS06ZNo2jRonh6erJ+/XoCAgKMWmODBg1o0aIFPj4+3L9/nwkTJuDn58fBgwdxdHTk+fPnqNVqsmTJIjl2tmzZeP78udVz37hxw2ydk5NTgoJcTlw7nI8J5ZpTh6CgoATv3+RG8+YNxTt2xOHNG4q7uHB14ULC03ECdUpg6ZmQlhSTNU6+16QJb5Jxjrly5Upw+9Pt2wl6j/rUJZbwb7Og++6772jdujUVKlSgS5cuxk4FN27cYM2aNbx9+5aZM2faNbkFCxbw1VdfUaxYMdRqNaVLl6Zdu3acO3cOgLYmYbPFixenTJkylCxZkt27d+Pn52fXuUyx9KG8ffvW5ioYH2uVkN27d9OjRw9j5/E///yT4cOHp0nwT8eOHcmcOXOK5nem1ffs5uZGblmZppTEecAAHN68AUAdFkaBf/4h8pNPUu38aU26q4yi1+N8/75kVbYOHciaLVuyneJ1AhodQJ7nz4lMT5/JO2Kz6bJGjRps2LCBnDlzMmfOHAYOHMjAgQP59ddfyZUrFxs2bKB69ep2nTxfvnzs2LGDR48ecenSJQ4cOEB0dDR5ZVFBceTIkYOcOXMaUxmyZ8+OXq835vTF8eLFC7Jnz27XXN5XTCNTs2bNSunSpRk1ahShoaEpfu42bdoYX0psoWTJknb5cd8VURRZvnw5DRs2xNvbm9y5c1OrVi1mzZpFUGylidTiyJEjeHh4mN2r6QG5mUy7bFkazUQBQHjwAMHEkmDInBkxa9ZkPcfbGjUQTV7i9CVKSLar7fhdvw/YlTBeq1YtDh8+zLNnz4wFcnPnzv3OHZxdXV1xdXUlMDCQ/fv3M378eIvjXr16xZMnT4znK1OmDA4ODvj7+9O+fXsAHj16xLVr16z2XvsQiYtMjY6ONgYNhYWFGbsTmKLT6VCr1cmS5O/s7Iyzs/M7Hyel+PLLL9m6dSuDBw9m8uTJZM2alatXr/LHH3+QNWtWPv3007SeYpojPHlits5QrFgazCT1UZ85g2bbNtzz5IF0pL2or12TLBsKF7ar8okt6DJnJuTAARzWr0dfujQGHx8y1qkTP4cPTNAlqXuBp6cnFSpUoEKFCu8k5Pbv38/evXu5e/cu/v7+NG/enEKFCvHpp58SEhLCqFGjCAgI4N69exw5coROnTqRLVs2mjdvDoC7uzvdunVjzJgxHDx4kPPnz/Pll19SvHhx6ph8aR86cZGp3t7etG/fnvbt27N9+3YAfvrpJ6pWrcqff/5JmTJlyJ49O6Ghobx9+5aBAwfi6+uLt7c3n3zyCWfPnpUcd/Xq1ZQoUYIcOXLQtWtXM7/Rn3/+aWbr37NnD/Xr18fLy4t8+fLRsWNHIiIiaNasGQ8ePOCHH34waqBxnDp1ik8++YQcOXJQtGhRBg8eLNG4wsLC6NevH7ly5aJgwYJMnz490c9k06ZNrFu3jt9//53hw4dTvnx5fHx8aNy4MevXr6dZbBFbg8HA1KlTKV68ONmzZ6datWrGzw7g3r17eHh4mH02Hh4ebNmyRTJmy5YttGrVihw5clC5cmX8Ywvn3rt3jxYtWgAxLZE8PDzo169foteQGmj27DFbJ5p0f/9QUd25g2ujRjjNnEnBwYMtfg5pher6dcmyoVChFDmPoVgxIkePRteyJYaiRRFNUrdUDx8imAT+ve8kKOhu3bqFp6cnP/zwQ4IH+eGHH/Dy8rK7H11QUBDDhg2jUqVK9O3bl6pVq7JhwwYcHBxQq9VcvnyZLl26UKFCBfr164evry979uwxNuyEmAd5s2bN+Oyzz2jSpAmurq6sWbMm2XLoPDzczf68vDwtrk+Ov+TAycmJ6Oho4/K9e/dYv349S5cu5ejRozg6OtKxY0eePHnC2rVrOXz4MNWqVcPPz4+nT58CcPr0afr370/Pnj05cuQIjRo1YtKkSQmed9++fXTu3Jm6dety8OBBtm3bRo0aNTAYDKxcuZJcuXIxfPhwrl27xrXYt9ZLly7Rpk0bmjZtytGjR1mxYgUXL17k66+/Nh73hx9+4ODBgyxfvpwtW7Zw4cIFjsvq/8lZt24dvr6+Vn25cYJ23rx5zJkzh7Fjx3L8+HGaNWtGt27duHDhQqKfs5wJEybw5ZdfcvToUcqWLUuvXr0ICQnB29ub5bHmwZMnT3Lt2jUmT55s9/FTgrj8KQkfaHUMUzQ7diCYRImnJ3OtXKPTxzYkTlEcHdHLNPkPSatL0HS5YMECsmXLlqigGzVqFJs3b2bBggWJPgxNad26Na1bt7a4zdnZmY0bNyZ6DEdHR37++Wdjv7OPnTNnzrB+/XpJu6SoqCgWLFhg9FseOnSIixcvcvPmTaPpcdSoUezatYu1a9cycOBA5s+fT+3atRk6dCgQk7Zx8eJFVqxYYfXcP//8My1btjTmQEJMrzuI6UOnUqnImDGjxAowe/ZsWrduzTfffGNcN336dGrVqsWLFy9wdnZmxYoV/Prrr8a8yLlz51IsEfPa7du3bQow+PXXX/n666+Npu/vv/+e48eP8+uvvzJ79uxE9zelf//+NG3aFIDRo0ezZs0aLl68SNWqVY2dPbJly2YWJZxmhIWhOXjQbLVpQ88PFZUs2ENz5AjodKBJUvnfZEVlyXSZCujLlkVjItzUZ8+ia9gwVc6d0iSo0fn7+9OmTRuzaiRyHB0dadOmDfv27UvWySnYxr59+8iVKxeenp40bNiQatWqMXXqVOP2nDlzSoJzzp8/T1hYGL6+vuTKlcv4d+XKFe7Etuy4du0aFStWlJxHviznwoULdvcjPH/+POvWrZPMo0mTJkBMxZ07d+4QFRVFpUqVjPtkyJCB4sWLJ3hcUUy8zWJQUBBPnjyhSpUqkvVVq1bl6tWrdl0HIJlTjhw5ACR5n+kNzaFDkqCHOD4KQSdrwisEBaE2KT+YZoiiuUaXQqZLOfJWPx+NRvfgwQObw259fX2VDs5pRLVq1Zg1axYajYYcOXLg4OAg2e7q6ipZNhgMZM+enZ0WzFYZU9k/YzAY6N69O/379zfbliNHDm7evJmk4xYoUIDrMl+HPcQF66hiuzabCk5Ts7Appp973P62CNy0wmHXLssbPgZB9/Ch2TrNwYPoTV6o0gLh2TNj7zkA0dUVMZFUgOTioxV0Wq3W5oTZyMhINOlA7U9uAgPNy+Sktzw6FxcXi6XZrFG6dGmeP3+OSqWymspRuHBhTp8+LVknX5ZTqlQpDh06RI8ePSxu12q1ZtVzSpcuzZUrV6zOP1++fDg4OPDPP/8Y5xoaGsrly5etzh2gffv29OrVi61bt1r00wUGBuLh4UGOHDk4efKkRBM9ceIEhWPNRVljw7rjfJcAFy9etHpea8RZReypHpSiGAxorAg64SPw0QkWXso1/v5EDh+eBrOJR2621BcqlOwRl9aIC0iJa+qqevwY4dkzxHeMqk8PJGi6zJ8/v6QcV0IcP37croetQtpRp04dqlSpQpcuXYxRrwEBAUyaNMkY5PHll19y8OBBZsyYwa1bt1i5ciV///13gscdMmQImzdvZsKECVy9epUrV64wd+5cwmI1hDx58nDixAkeP35szCcbOHAg//77L//73/84f/48t2/fZteuXQwaNAiIMVN269aNsWPH4u/vz5UrV/j6668xGAwJzqV169a0adOGPn36MHXqVP7991/u37/Pvn376NChgzGy8ptvvuHXX39l/fr13Lx5k4kTJ3LixAmjz9DZ2ZmKFSsya9Ysrly5wqlTpyQ+SFvJnTs3giCwe/duXr58SUhIiN3HSE7U586hevbM4jYhFXIw05SQEFSxCfKmqP/5B4KD02BCJnOQ++dSyWwJgFb7webTJSjoWrRowdatWxMVdidPnrT65qyQ/hAEgXXr1lGzZk0GDhxIxYoV+eyzz7h586bRt1SxYkXmzJnD4sWLqV69Otu3b2fEiBEJHrdRo0asXLmSvXv3UqtWLZo1a8aRI0eM5r/vvvuOhw8fUrZsWQoUKADEBKvs2LGD+/fv07x5c2rUqMH48ePJZlIF4scff6RGjRp07dqVFi1aULRoUapVq5boNS5atIjJkyeze/duWrRoQfXq1Rk3bhzVq1c33qt9+/blm2++YcyYMVStWpXt27ezfPlySpYsaTzWr7/+CkC9evX43//+lyRBlzNnTkaOHMmECRMoWLAgw4YNs/sYyYn68GHrGz9wjU7un4tD0OnQHDuWyrORYpZakEqBKHGYmS9laTXvK0JgYKBVJ0JoaCi1atXi6dOnDBkyhI4dO0ryph4/fsyaNWuYMWMG2bNn5/Dhw2TIkCFVJp7cvH37Fnd328L705vpMjVQrjn1sOdeTCrO/fqhTaCp59sXL0Dm6/1Q0OzZg2uHDha3RX75JbqaNdGuXo2+ePEYU2YqumRcW7SIiQCNJfTPP9HF5nwmJ9bKnjksX47LgAHG5egmTQizo/VaeiXBb9DV1ZVNmzbRtWtXfvzxRyZMmICbmxsZMmQgJCSEoKAgRFGkRIkSrFix4r0VcgoKHxuqxIJ8wsIghYVtWmFNo4OYfDrH2P6XDtu3Q3Q0kWPGpNbU0l6jK1VKsqy6dStVz59SJFoZJU+ePBw8eJDFixfTrl07fHx80Gq1+Pj40K5dOxYvXszBgwcTDAxQUFBIXyQm6N7LFIPIyBhBkUikq6VAFOM2WfCd4/z5CC9fJsv0EiUwUOI3FbVas27gKY0hXz7Jsuru3Zj8wvccm3RylUqVYHK3goLC+4Pw6pUkGEN0ckLMnl2SRC2Eh5N+EyPMEe7eJUPDhqhevEBXuTKh27dbNTkmpNGZHTc8HO28eUQmUjQjOTALRClQIPUT2N3dMWTNiipWuAvR0QgPHyK+54pMkmpdKigovL/ItTlD/vyIcrfDexZ56fzDD6hik/M1p06hMalXKkcu6IIT6aPp+PvvENuaKiUxSy1IZbNlHAZZ9Lw6tojE+4wi6BQUPjJUsgaehoIFEV1cJOvep1w64elTNDt2SNapE8h1lAu6pz16IJoE3ugqVMBgUqZNCA6OEXYpjDqVijknhlzQfQh+ug8vw/sdEEUxWdrXKCgkldSopCLX6PQFC6KR55W9Rz467cqVkgLNAGpr3bijohBMkv9FQSCocmXCVqzAYc0a9OXLE/XFFzj+9htOJu3CtPPmxfRvEwT0pUqht7PUnS2kdSCK8bxyQRfb//N9RhF0scT1w/Pw8FCEnUKaIIoigYGBKV6GTS03XRYogCjTgN6bYBS93mLnAbnWalz/6BGCycuE6OWF6OCArkkTdLE1VgEiP/8cx5kzjeW4VG/e4Dx6tHF7+PTpRH3+eXJdBQDq//6TLKeZ6TI2xzUORdB9QGg0GjJmzGhT5+mgoCDc3NxSYVbpB+WaU4eMGTOmeCk9Mx+dJdPleyLoNAcOWAwuUd2+DQYDqKTeGUHWtcCQO7flA7u7E/nllzhZ6YrisGJFsgo64dkzVI8fG5dFR0dFo0tGFEFngkajsSlR9/nz5+S29gP5QFGu+QNBrzd7cOl9fUEm6N4X06V2yRKL64WIiJhowTx5JOvlQtGqoAOi+vdHu3q1xQLQ6lu3YtIYksn6Iy+1pS9RIs0S9vWWUgz0ekimHp9pgd2C7urVq9y9e5fAwECL/oTOnTsny8QUFBSSH+HBA2PRXgBD1qzg4fH+aHQGA9o//kB98iSCXo9m926rQ9U3b6J7B0EnZspEyI4dOGzciOrVK7R//GHMsxOCgxFevkQ0KVX3LshLbenLlk2W4yYJDw8MWbKgiq1HK0RFxbw0+Pgk+6nUZ89iyJ0bMbZ4ekphs6C7c+cOffr04cyZM1Yd5oIgKIJOQSGdITx4gPrMGfTVqpkFaRjiykC9J1GXjlOn4mRjd3bVzZtQr550nUzQiYlo7GKePETFFhjXHDiA+tKl+GPduoU+pQRd6dLJctzEsKaUGvLnNwo6ANWdO+iTW9CJIi5t26J6/RqDtzf6smUJmzMHPDyS9zzYIegGDRrE5cuX+emnn6hatSoeKTAZBQWF5EV18SIZ6tdHiIrCkCMH0e3aSbbHBR6IsZ3mjaTDPDrh9Wsc58yxut3g44Pq3j3jsqWAFHs0OrPjFyggFXS3b6OXNe1NKurz5yXLKanRvXkjsGlTVo4cceWff9SUKaNnxYowsmWLV2AM+fPDP//Ez+/2bfR16iTrPIT791G9fg3E9AcUAgMhhQKxbBZ0p06dYvDgwXz55ZcpMhEFBYXkx3H69Pj+Yk+eoJ07V7JdH6vRibLmvOlRo9POm2e1hZC+ZEkiv/4aF5Pnk6UyZ/LyXwY7mprq8+fH1GuWXEEawpMnqExTHpycMBQpkizHNkWng9GjnfjjDy3R0fFBVidPahg82JkVK+LN1amRS2fmlyxZMsX8gDYnjGfJkuWji7pTUHivCQ426yIuyPr4GXx9Y/4j1+hS0EenOn8e16ZNyViihPHPtUED1CZV+814+9ZYbDmOyN69CV26lJDNmwnZty/mQWmCWS6dwYDq0SPpKns0uhSKRrT4wE/myFtRhEGDnPntN0eio81tldu2OXD8eLyQsXSt6oMHcW3aFJeuXc2iV5OC2XWnoBZr86fZq1cv1q1bxxdffIH6PY6+UVD4WHDYudOsSLGcOEGXWsEowr17uLZubTRZxaF6+BCXzz8n+PJliw95x0WLjDltAIZMmYgYOxZMSpcZ8udHFARjnpzq4cOY3nqxQlx49gwhOjp+vIdHjKnMRJtKiBQTdHL/XCIlyZLC+PGOrFypTXDMqFFO7NsXikplnkunPnsW1y5d4u8LQSBsxYp3mlNqBuDYrNHlzZsXnU5H9erVmTVrFuvXr2fTpk1mf/YQHBzMiBEjKFGiBF5eXjRq1Ih///0XgOjoaMaMGUO1atXImTMnhQsXpnfv3jyQmR6aNWuGh4eH5K9Xr152zUNB4UPEYcOGBLeLarWxWn2qCLqwMFw//dRMyMWhev5cUljaSGiomck1ql8/iZADwMnJPJ3AxORmbyCKHLMakLdvJ9opwRbMNJtkFHSiCHPmaPnlF2lfxaxZo/jii0jJun//1bBhQ4xxVi8X6k+fSu4Jzf79MSkHtmIwoD59OkZrNxhAFFP0uuXYrNH17t3b+P+xY8daHCMIgl0dDgYMGMClS5eYN28euXLlYu3atbRq1YqTJ0/i6urK+fPnGTp0KCVLliQoKIhRo0bRrl07jh07Jkmq/fTTTxltUrXgY2sQqqAgR3jzJuZhlAAGHx/Qxr7ly/PokttHJ4o4f/21WfUPOcKDByB7yGqXLpVEAIpubkT26WNxf72vrzQg5eZNDCVKxPz/HQJRAMQcORCdnY3+SyEoCOHVq3cLjbf0wE8mzebuXYHhw53Zs0eaj5cli4H586/RoEEenj5VsW1b/PZx45xo3jwaZw8PDJkzW30pEcLCUF2/jqFo0QTnILx+jcOff6JdvNhYHDqqdWsixoxBZVIoW8yY0UyLTE5sFnTbtm1L1hOHh4ezdetWli9fTs2aNQEYOXIku3btYvHixYwaNYrNmzdL9vnll1+oUqUK165do3jx4sb1Li4ueHp6Juv8FBTeZzRbtyKY9BETVSrr/jnMoy6tBX0kFe28eWg3bpSsi27RAvR6HEwKMqvu30eiJ8TmzZkS2bu31RB0g68vmAh49c2bxH0KqsuXpWPtLQYgCBjy5UNtchzV7dvo30HQCY8fo3r+3LgsOju/czFngwFmzXJkyhRHIiKk/rgMGUTWrw8jQ4YYbW7cuAh27dIY/XYPH6po1syVmTPDqZo/v1VBB7E5cAkIOs327bj06WN2L2k3bTLTvPWlSplVsUlObBZ0NWrUSNYT63Q69Hq9mfbl7OzMiRMnLO4THBwMYJbasGHDBjZs2ED27Nlp0KAB3377bYrXC1RQSM9oZWbLqG++wWHVKmMrG5AKOmRRl8mq0el0OE6bJlmlL1aMsHnzcJw2TSroZFqX5uBB1HfvGpdFR8cYs6UVjHmBccczCUiRB+YkRXMyFCggFXS3bqGvVMnu48Rhps2VKvVOgSg6HQwY4MyqVeb+OCcnkRUrwihbVk/cx5I/v4HevaOYN8/ROO7ffzXUrZuBgb4/MJXWqDGYHStu7tFdulieiMGA8/DhVl+YzKJ/UzhB3u5PVK/Xc/78ee7H2tLz5MlDmTJlUNkpjTNmzEilSpWYNm0aRYsWxdPTk/Xr1xMQEEB+mekCICoqilGjRtGkSRNy5cplXN++fXty586Nl5cXV69eZdy4cVy6dClBf+ENa5XN7SA5jvG+oVzz+4HDy5eUkkUwXq9RAw8g96xZxnX3Cxbkbez1OT1/TgmT8dGBgcl27Y4PH1LSRDPQOztzeeJEIp88IZujI6ZpyCGXLnHX5LwF5szBVAS/rluXO4GBVvvDZXRywrRCZNR//3Hjxg20T55QyiQHTlSpuFagAPrYc9l6rbkyZSKHyXLg6dM8rlDBpn0tkXP/fsn1vcqblwdJ/NwjIwVGjcrPwYPmQq5MmWC+/fYe3t4RRiEXd81t26rZurUojx7FKx16vcCMa364MobxjLF4vugTJ6x+bs43blBcFuFqiiDrWv7Iy4vX73C/FZS94MixS9Bt3LiR77//nmfPnhmrowiCgKenJ5MmTbK7A/mCBQv46quvKFasGGq1mtKlS9OuXTvOyd5ydDodffr04e3bt6xevVqyrWfPnsb/Fy9enLx581K/fn3OnTtHGSvOzcQ+lMR4uHMnPm5u6KtXf6fjvE/cuHHjnT+394339Zq1+/dLKvTrS5UiT8OGUL8+kQYDmv37iW7enOw9e5I9tiyGoJU+HB11umS7do2scadYpgx56taN2Va+vGSbx9u3xvMKT56Q8fBh6bwGDEhwXoLMBOv68CEFfX3R+vtL1usrVyZ/xYqAfd+zQ/nysHy5cTnr27e4vsPn5CLTYDPWqZOkzz0yEjp2dOHgQak/LnNmAxMmRNC5swFBiDfVyq95//4ovv1WzZYt0v2nM4SBzCIL5iZM15s3KZgvn0UNVCvTnnVVqqC6f19SuNqUbE2bkiUFfXQ2q2Hbt2+nd+/euLu7M2XKFDZv3szmzZuZMmUKHh4e9O7dmx2y5oeJkS9fPnbs2MGjR4+4dOkSBw4cIDo6mrwmbdt1Oh2ff/45ly5dYsuWLWTOnDnBY5YtWxa1Ws3tlKi4HRSE03ffUaxrV1z69n1vCt8qfFw4/P23ZDmqbduY/6hURPz4IyHHjxP53XfS2k8pWNTZrNGriclU7iczNV3K+8zpCxdGX7VqgucSc+aURJAKb98ivHyJRvbgjW7a1PYLMJ17MidSm1VESWLk4cyZjmZCLnduA3v3htKlS3Sitae9vESWLQtjzZpQMmeON1WG4cpcvgJAdHfHYPL8FcLDzbqix6E5eFCyHN2qFVHdulkcK7q5GaN/UwqbBd306dMpU6YMBw8e5IsvvqB27drUrl2bL774goMHD1KqVCmmyezwtuLq6oqXlxeBgYHs37+fTz75BIhJMfjss8+4dOkS27Ztsyng5NKlS+j1+uQPTgkLI2O1ajj+9huCXo/qwQMcZ8xI3nMoKLwrgYGoZT5unZ9forulZIdxuTDQm2gSckEnPHoUE7Zuoc9cVM+eiXcLUKnM0wDOnkUjM+Wa9p6zB7Njx3UxSALCs2fSQBQnJzMfoy28eSMwd66jZF3hwnp27QqhQAHL/jVrNGmiY9AgadrBHL4hDGfCf/wRfawWHIc8Fw6AyEg0x49LVunq1iWqe3dECy4ufenSKRqIAnYIuitXrtChQweLofuOjo507NiRK1eu2HXy/fv3s3fvXu7evYu/vz/NmzenUKFCfPrpp+h0Onr06MHp06dZuHAhgiDw7Nkznj17Rnjsj/DOnTtMmTKFs2fPcu/ePfbs2cPnn39OqVKlqJJMNeiMuLjERImZ4Dh7tsUyQwoKaYXD3r1SLahIEdveluVRl+HhMeF7yYBZIWnTIJgMGTBkyhR/Xp0O4elTNPv3S9rjiE5ORNlYMF4vExZO330nSRTX58+fJIEC8SkGxvkGBSHIu7PbiFmj1WLFkhSIMmeOlqCg+BeAzJkNbN8eSq5cSRPAPXtG4eYWv+9LsjHvm7NEd+9uVmxarpECqE+dkrwoGXLmxFCoEGKuXOgaNTIbnxqdGmwWdM7OzrwyyWWR8/LlS5zlZYQSISgoiGHDhlGpUiX69u1L1apV2bBhAw4ODjx69IgdO3bw5MkT6tSpQ+HChY1/G2PDlB0cHDh06BBt2rShYsWKfPvtt9StW5ctW7akSPWWiJEjMXh5GZeFqCichg1LlqRRBYXkwMxEZ6vmolKZF3ZOJq3OrNGrqaDDPHFb9fAhWlnVjejWrW2uaq+vXFmyLO+ormvaNOl95FQqsxeHpJovVbKu7nH5fvbw4oXA/PlSbW7gwEiyZk36M8nNDT7/XKrVzdpckOhoc6FkSaOTmy11deoYP++ozz4zG5+SieLGOdk6sHbt2ixYsIC6detSrVo1ybaTJ0/y+++/06BBA7tO3rp1a6sBLD4+PgRaiayKw9vb226/4Dvh5kbEhAm4mCTPO/j7o9myBV2rVqk3DwUFS0RH47B3r2SVPSY602RoiNHq5MWe7SYkBNWTJ/HnUKsxmPjgIaawsvrCBeOy6sED1MeOScZEmQSdJUZU164xCcrXr1vcbrPwt4LFXDqZSc8W1DJBJ6/VaQszZzoSFhYvtLNli0kXeFf69o3it98ciYyMOfaDByo2bXKgY80yknHq//6D6GhJk1iNLPBHFxt4BKBr0ACDt7dRWxcFAd07RK3ais0a3bhx43BxcaF58+bUrVuXPn360KdPH+rWrcsnn3yCq6ur1YopHxLRbdsSJPtinL/7DkJC0mhGCgoxqI8fl9aDzJLFvgewPCAlGZLGzbS5vHnjq7HErZNpdOoTJySJyqKrK3p7HoYZMhC2ahWihSL0orv7O7fWkVfwSKpGZ2a6tFOje/JEYNEi6Wc5eHCkWUpkUvD0FOnSRSowlyzRIubIIbVqRUSguno1fvnNG7PcQF3t2vELajVhv/6KGHsPRH3zjVnyeEpgs6DLkycPR48epW/fvoSEhLB161a2bt1KSEgI/fv358iRI+RJhQmnOYLA/eHDEU1s6arHj3GaOjUNJ6WgYJ4QrWvUyK62JykRkCI3G8rNlmAu6ORRo/pSpexu32Lw9SXsjz8QZSbK6EaNJNpHUjCrAylLn7CJ8HCzaFS9SbUnW/jxRydJ5ZOcOQ189tm7a3NxfPWV9Fj//KMmJMTc1Ggq2NSHD0tTW4oXR8yeXTJeX6cOQffuEXT5MhHjxyfbfBPCrlCXrFmzMmnSJP755x+ePn3K06dP+eeff5gwYQJZU7gVenoiIl8+or76SrJO+9tvqOwMxlFQSDZEMen+ubhDpEBh54RSC4zr5D66Z88ky0n14egaNyZy1CjJOquVPOzALPLSJBHdVtRXrkhKsunz5YtxjtnI0aNqs+onQ4ZEkpxlfn19DRQoEB/YpNMJnDihSVDQJWS2lODsjJgzZ3JNNVFSNqbzAyZi2DAMJhVaBJ0OZyUwRSGNUF2/biyaCyBqtejq1bPvICnQky6h1II4EjNdvUtUXuTgwYTNmkV0y5aE/fab9QevHRiKFpVoiurLl1HJzHWJ8S6BKFFRMGSI9LsqWlRP9+7Jp83FUbu2tILJoUMas+/DYfNm1KdPo9m/36z0XHJ83smB1WCUKVOmIAgCQ4cORaVSMWXKlEQPJggCw4cPT9YJplsyZCB80iRce/QwrtIcPYrDxo1ExyXoKiikEg7r10uWdTVrxvRaswOzLuPJIOjMUgssVL9IrLjyO0XlCQLRPXoQbfI7fVfEbNnQ1auHg0nxaO3SpUTMnJnwVF6+jIluzZz5nQJR5s515No1qSl3+vTwd7XIWqRWLR2LF8dHdR46pEE/sByiWm1MY1G9eoVrs2YQHS3RUkUnJ3SJJPinFlYF3eTJkxEEgUGDBqHVapk8eXKiB/uoBB0xibjR9erhcOCAcZ3jlCkxodApnACpoBCH+sgRs+IFSUqITu70AlE00+gs5a+JWbKYRXwat2XIYNHcmdZE9ewpFXTr1xPx449WXy4cVq3C+X//Q4iMJPzHH5MciHLvnsDUqdJ0gq5do6hWzY7ecHZQs6b0uBcvqnmpyo5jv344/vqrcb0QGSnflYgRI8wDnNIIq0/jN2/e8Pr1a7Sx0TFv3rxJ9O91Ai0dPkgEgYipUxFNHOXq69fRJNLSyGHjRjJUqIBr06ZKwrnCOyHcv49Lz56SJHHRzY3oNm3sPpaZj+4doy6Fp08RTKKRxQwZEC1VLBIEq1pdSrdvSSq6Jk2k0YchIWj/+svqeKdJk4zCwGn0aNSxDabjsFWjmz3bkfDweLNppkwGxo1LuIv8u5Ali0jJklJhd/Somojx44n43/8s7iOqVIRPmEDUwIEpNi97SX930HuGwdeX6HbtJOucpk2z6qsTXr+OaUB58yaaEydwkjnLFRRsJiwM165dJU1JAcIWLEDMksXuwyV31KVZVKGvr9VEbauCLhWqZiQJBweiunaVrNIuWWL5dx8SIqnyIogiQlS8P83g4YHo7Z3oKQ0G+PtvqX1y3LgIsmRJ2bgAS346VCoix4yJudcc4zVM0c2NsDVriPr666Qn5acANgu6zJkz81cCbywbN25MtODyh0rk4MFS5/TFi2j27LE4Vh0QIG1Jf/RospVaUvi4cFywQJJoDRDx3XcxlT+SQjIXdjZLLUig7JZVQZcKVTOSSlT37ma/e7mmBuZRpHIMJUrYJBTOnFHz7Fn8IztjRpGOHaMT2CN5sCjoYonu2JGQ3buJbt6cqLZtCdm3z2KZr7TGZkEnJhJNaDAYENKRBE9NDIULmxXOdbSi1clL5gghIahMGksqKNiKRlYFJbp5cyKHDk3y8eJKgB2gLn1YwPITRd/pHcwsWTyBNizyMmBxpCeNThTh4UOBuJx8MU8es4e6dskSs/0Ek8owlrDVbLljhzSkokGDaBwdrQxORqpW1aHRxD/Lbt1S8/Bh/LPeUKYMYStXEr5o0Tt3R08p7DJdJiTITp8+bdb5+2MiYsgQybLmn39Qy3ppgXlHYTAPNVZQsAXBpFs4xNRifSd/lqsr5yjNJ+zgD/rQd0dbNm5MeiifmaCzU6MT3dzMctbSAlGE7ds11KqVgRIl3Che3I3ly2M+F3lpMoetW2NKYpmQmEZnayDK9u3S7+KTT3RWRiYvGTJAhQpSP93hw0nvgp4WJPirmDdvHqVLl6Z0bMXqkSNHGpdN//Lmzcvvv/9O48aNU2XS6RFDqVJEy67fyULbIkuCTh5qrKBgC3JBZzHQww5EZ2d+YiSRxGcdb9qUNEEnvHghqQcJsT46K1gSdOkhECUgQE3t2hn49FNXLl6MCToLDhYYMMCFsWMdiarfEIOJP1QICkIta1GTHBrdjRsqrl+PD3rTaEQaNkx5s2UctWpJher69Q7oUkfOJgsJiuVs2bJRpEgRAO7fv0+OHDnIkSOHZIwgCLi6ulKmTBl6mxQ7/hiJHDoUh927jcuaI0dQnzplrKYuPHli8e1OEXQKdhMVhcqk6LmoUiGatLtJCnfDPVmPNLDq6tXEBY1w9y7aFSuMTVOFoCA0+/dLWuNAwqZLi4Iujc2Wp0+radHC1VjYWM7MmU7cuaNmWb1muP8V33XcYdcu9Cb1HVVPn1o9h8HbG0PRoonORW62rFFDZ2szh2Shdm0dplUODxxwoFs3FxYvDjPLSkmPJCjo2rVrR7vYiMLmzZszbNgwapsW6FSQoK9YEV3t2mgOHTKuc5w+nbB16wArTQpJWgkhhY8bQRZpKWbJYnc9SDm/Ha+AAekx7txRERGB9dJSUVG4duyI2kqn6Tj0hQrF2MCsIObIgajRIJioCWkZiPLkiUDXri5WhVwcW7Y4MKTedywkXtBpdu6ESZOMASaCTNBF/PADqosXEYKDiRw50qbamzt2pI3ZMo7KlfUUK6bn8uX4+2PnTgfatHFl9erQVBW6ScFmu8Dff/+tCDkbiJAFAzjs2WMsD2TJbAkx/beS2rxR4ePEzGyZLds7He/tW1h2tLDZeoNB4MYN648JdUBAokJOzJiRiAkTEp6AhfY9+nLlEt4nhYiIgG7dXHj6VHrdLVtGs3hxGNmzSyN0lh0uxF2HeLOs+u5dVCafiVyj05ctS/jSpYRt2GBTV4bnzwUCAqQvIJ98knpmS4jpB7t8eRi5c0uv/cQJDcOGpX+Vzm4DeHR0NJcuXeLEiRMcO3bM7O9jR1+jBjpZ40en6dMB64IOlIAUBftQvXwpWRbfsaj68uVagsMtaxZXr1rXFK1ZKQD0BQoQPmkSQRcv2hRyHtWvX/z/W7WyrTO6Fa5cUbFypQOPH9sXCX7jhoqePV04fVpq7Bo4MJJly8Jo0yaafftCJA98nU7gZ8+fJeNNC2zLNTrTRHNb2LVLgyjGX0fp0nq8vVO/pq6vr4Hdu0MoVkwamLJhgwPPn6fviHubQ2dEUeTHH3/kjz/+IDSBigkfXXUUOYJA5NChaNq3N65y2LYN1ZUrCT4U1Bcvoq9VKzVmqPABINfoDO+g0UVHw4IF1uPUE/LTqc+flyxHdemCrlYtDPnyxfTCsyOYJOrzz9FVrIgQGIi+Rg2b95Ozf7+GLl1izI4ZM4ps2BBKpUoJl8i6ckXF2LFO7N5tLuwbNIhm9Oj46iN58oh8+20EX38dn3e4+GkzRpMdT54D4LBzJ1GDBgHmGp0oi3NIjG3bpHNq1ix1tTlTcuYU2bEjhPr1M3DrVswLkMEgsHGjA337Jn9R6eTC5rtw5syZ/PLLL7Rt25b58+cjiiJjx47ll19+oWjRopQsWZJNmzal5FzfG3QNGqCPjVSNw3nQIFSyh5MpSkCKgj2YmS7fQaPbvl3Dw4fWHwVXrtiu0UV160Z0p04xAVhJiJg0lCoV88KXxGjL588F+vZ1NvrWgoMFOnVy4dYt68e7fduJxo0zWBRyBQroWbgwzMz92aFDNLlyxWt1EToHZhFf8kodEBBTxDk4WFoGzdER0Q6H1ps3Av7+Un0kLQUdgIcHZp0S1q1LgYrSyYjNd9PKlSvx8/Nj5syZNGjQAIDSpUvTo0cPDhw4gF6v5+jRoyk20fcKQTDPqzt1SrIsyjI95UVeFRQSQpCbLt9Bo1uyRHovViRAsnztmpXHxNu3qE2KNosqlV1V+JMbgwH693fmxQvpfF+/VtGunQsvXpib1wwGmDTJh6Ag820FC+pZty7MYqCFVgtffy0tZDxX9Q1viekpJ4gimj17zKKsRU9Pu0pjbdumQaeLH1+okJ5ixdK+klLbttEIQrz59N9/Ndy8mX4rSto8s4cPHxqDUVSxb1uRsUVKHR0d6dixI6tXr06BKb6f6Jo3R5eAozlaVklFde1aTKMpBQUbkFsHkmq6vH1bJSnpBDCbAZLluMhLOXKzpSGRyMqUZsECLfv2WdYs7txR06mTC3Kvy7JlWs6fl3YcKFtWx6JFYZw4EUKBAtaFSvfuUWTOHL89yJCRecT7GR127jTLoTPYabaU5zG2bh2dLkpIenuLVK8uNQenZ63OZkHn4eFBROzd7ubmhlar5dGjR8btjo6OdvvngoODGTFiBCVKlMDLy4tGjRrxr0mtOFEU+emnnyhSpAheXl40a9aMK7Iu3oGBgfTp04c8efKQJ08e+vTpQ6BJflGaoVIRPm2apBaeKbp69aSNW6OjJZFaCgoJYabRJdF0uWyZtEt1VY5ThVPkJb6Jq8EgcP26+aNCLujSMh3gv/9UjBkjzYFwdZUGbJw5o+Gzz1yMhUuePhXM9mncOJoDB0Jp2zYaTSIRDK6umPmlZjKICGI0ZM2hQ6hkgk60IxDl5UvBrAJJ69Zpa7Y0pUMH6bX/9ZdDuu07bbOgK1q0KBdj/UgqlYpy5cqxaNEiHj16xIMHD1i6dCkFEyjxY4kBAwZw4MAB5s2bx/Hjx6lbty6tWrXi8ePHAMyaNYu5c+cyZcoUDhw4QLZs2WjdujXBwcHGY/Tu3ZsLFy6wfv161q9fz4ULF/jyyy/tmkdKYShThqjPP7e4TV+mjFnpH8VPp2ArwvPnkuWkmC6jouDPP6Vv4X34HYDiSHM7LUVeyv1zaSXooqOhf38XoqLiXyrd3ESOHAmhbl2pYNizx4EBA5x5+FBg4EBnicnS1VXk55/D7dKY+vSJJEOG+Kf7M7xYSUxXAyEoCPU//0jGG+yoXrN1qwN6ffxkihXTU6RI2pst4/Dzi0arjb/2O3fUnD79brmcKYXNgq59+/Zcu3bNqNWNHj2amzdvUrJkSUqXLs2tW7cYPXq0zScODw9n69atjBkzhpo1a5I/f35GjhxJvnz5WLx4MaIoMm/ePAYNGkTLli0pVqwY8+bNIyQkhPWx3ZSvXbvGvn37mDlzJpUqVaJSpUr88ssv7N69mxuyFiFpRcSoURhkb9uiiwuGQoXM/BmKn07BVuSmy6QIuu3bHXj5Mv4R4OYm0k61ATAXdJb8dPJ0mbSqZDJ7tiMXLkgfsL/8Ek7+/AaWLQsz66e2erWWEiXczIJPvvsugjx57FNJPDygWzepZjOdIRiIEVDywtv2RFzK64ymJ20OYq69SRNp4np6NV/aLOg+/fRTDhw4gFNsiYSqVaty8uRJJk6cyOTJkzl+/Dj169e3+cQ6nQ69Xm88XhzOzs6cOHGCe/fu8ezZM+rVqyfZVq1aNU7FBnYEBASQIUMGKpvkrVWpUgVXV1fjmDTHw4OI8eMlq/QVKoBabS7oFI1OwRZE0cx0KX+ZsoWlS6Vmy44do3CKjZgvhrROpVnkZWAg6tu346eURoEoV66omDJFGkzTpk0UbdvGCAU3N1i/PhQfn4Q1oTJldHz5ZdJ85P36RaJWxwvIqxRlO82AmORxU2zNoXv2TODYMelnnt4EHUD79vLoSy1v36bRZBLgnUpQ582bl34mSZ72kDFjRipVqsS0adMoWrQonp6erF+/noCAAPLnz8+z2GilbLI31WzZsvEk1u79/PlzsmTJIumqIAgCWbNm5bnMtGNKcmh7dh2jfHl8/PzItnUrOjc3bnbrRsiNGzhmzIjk0XDuHDeuX09XDQtNSS9acmqSotcsikn6rlWhoZQziQ7ROzpy48kTSKCmoilhYSrWrs3OoUPukvX16t3EsMkJQkLMNLqLF/WSzyLjP/9gund4vnzcMPHZJ4W7d52YPj03gYEavvjiMbVqWX5i6vXw4IETd+86sWhRDonJ0sMjmr59L3PjhlTT+OUXRz7/vAhv3phrHJ6ekYwceYM7d5LeqbtBg3zs3h1f3PlnhtGCv83GPdDrCbbhnlq3Lhui6GZcLlQoDFG8RnLejslxb+fPL+DuXpq3b2NEydu3ApMmhdC7d8KFrJObxNxmNgu6MmXK0LZtW1q3bk0JG9tKJMaCBQv46quvKFasGGq1mtKlS9OuXTvOJVBBJDmw15co58aNG/YfY9kygl68QHR2JkfG2CivAgUQ3dwQYhtcaYKDKezg8E4VIVKKJF3ze05KXbNm/36cRoyICViaMQN99ep27a+6c0eyLGTPTkEb+oCJIixerGXSJEdevZIacypV0tG0qTeG2Aq9RZEGfT164IB3xuw4e8WIN+2OHdJrqlz5nT6ru3cFvv46g7Gx6LBhvixeHEarVvECKygoZv7z5jlKGpCaMmNGNJUqmf9+ChaETZsiaNtWbbz2cuV0dOgQTYUKV6lQ4d3aAX3/vQqTeu4coRanqERlWapGzgoVEmxXFMepU66S5c6dVcl6Lybnvf311zomTowXJWvW5OS77zLg7m59n9evBf77T4VWC05OIr6+hhQN2LXZdOnj48OsWbOoVasWVapUYerUqdyU9Zuyl3z58rFjxw4ePXrEpUuXOHDgANHR0eTNmxfPWKftC5kv4sWLF2TPnh2A7Nmz8+rVK0lTWFEUefnypXFMukEQELNnh4wmocwqlVlieUJlwhQ+ACIicO7TB/WNG6ivXcN5+HC7D5HUqih//61hyBBnMyEH0KdPjAnKEOtKcCWMfMSbJg2ouT9soXE5OQNRXrwQaNPGVSK8DAaB3r1d2LVLw9mzasaOdaRECTfGjnW2KuSaN49O0LxXpoyB06dDWLculDNngjlwIJS+faNwd0+4aootlCploE4d6bl/ZpjZOFtMlxERcOqU1Gzp55f+zJZx9OkTiYdHvGn47VuB33+3XGknLAyGD3fC1zcjfn4ZaNIkA3XqZOTs2ZQNYrFZ0G3ZsoWrV68ydepUsmTJwpQpU6hUqRK1atVi9uzZPIht0ZEUXF1d8fLyIjAwkP379/PJJ5/g4+ODp6cn/v7+xnERERGcOHHC6JOrVKkSISEhBATEvzUFBAQQGhoq8dulNFu3aujc2YUJExx59co+U5T8AZFQmTCF9x/N0aOoTDoPqC9dApMoYltIakFnS01U3dxEfvwx3OjTMpj4zOXmy+v7HkNs7mxyBaIEB0OHDi7cvm3+oNPpBDp1cqVu3QzMnOlkMak7jpw5DUyfnnjEZKZMIo0a6RLMj0sqAwZI/VWbaM1t4rVL0cmJBNWcWAIC1JKuCd7eBvLnTz/RlnLc3aF/f+m1z50r9dWJIpw8qaZmzQz8/rsjBoP0i0rpVj92pbJnzZqV3r17s337di5dusSECRNwdHRkzJgxlC5d2u7Gq/v372fv3r3cvXsXf39/mjdvTqFChfj0008RBIF+/foxa9Ystm7dyuXLl+nfvz+urq7G1kGFCxemQYMG/O9//yMgIICAgAD+97//0bhx41Qzsx0+rKZHDxd27nRg2jQnypbNyJw52rjnQaLIHxCKRvdhY1rsNw6VLGAhMZKSQ6fXY1ZK6ptvIrlwIYhvvokyCghTQScPSLkYXhDN3r0Ib95IgixEtdrmLtmSeYvQr58LZ8/aHyqQIYNI7do6eveOZNq0cA4dCsHTM22TuOrW1VG8eLx2aEDNTAbFL3t52eSTlefO1aqlS69ueyNffhmJu3v85x8YqKJx4wx8+aUz3bu7ULRoRpo0ia+PKcfRMWW/uyTXbPHy8qJ///7s3r2bWbNmkSFDBv6R5YwkRlBQEMOGDaNSpUr07duXqlWrsmHDBhxi+zMNHDiQfv36MWzYMOrWrcvTp0/ZuHEjGU3MfwsXLqREiRK0bduWtm3bUqJECRYsWJDUy7KLqCgYNsxZUlk8KEjghx+cqVw5A8ePJ66Om2l0586RbrMuFd4NUcQhGQRdUqqinDunJjAw/ueeKZOBsWMjzMpb6U1erUsgTXf5jf48WHEMh1WrpOcvUiRJr+Tr1jnw999SLbNevWh+/jnc6j6ZMhn47rsI/vsviC1bQpk2LYLevaPIli3tfzOCAF99JX3DXUwv3uAB2J5acOSIVNDVrJn+W3m7u5tf+9Wratau1bJ1q4NZyyOI0cIrVtRRsqQeN7eU/f6SHHV57NgxNm3axNatW3n58iXu7u507drVrmO0bt2a1q1bW90uCAIjR45k5MiRVsd4eHjw+++/23Xe5GL+fC3XrlkWZnfvqmne3JX//S+Sb7+NRKu1OAxDvnySgBQhKAjVnTsY8r+bc1wh/aG6eBHVw4fm6+3V6JJQ0Hn/fulPvU4dncU+raYaXRN24UwY4cTkHISQkV57u3PwnLQLefQnn9g6dSNPnggMHy4VjqVK6Vm+PIwMGWI00FGjnNDpBFxcROrV0/HJJ9H4+UWnZZWxRGnXLprx34fz9E3MtYWSgQV8yQim2JQsHhICZ85Iv5j3QdBBjFY3d64jb98mrn5+/nkk48ZFpNp3aZdG988//zBy5EiKFStGixYtWLduHXXq1GH16tXcuHGD2bNnp9Q80x2PHglMmWKt7XIMBoPA9OlONG7syrNnVr58QVD8dB8JlrQ5SAbTpQ0andxsWbeu5YenwUQzy8ZLpiMtTn7CUIWfnsVX+xFdXIjq2zfR85siijBokLPkgejoKLJwYZjxwde3bxRnzgSzb18It24FsXJlGF26pG8hBzHFnvt8Ji2oOYdviMLBpvJfJ09KizgXKJA2veeSgrs7LFwYRo4clv2JTk4iNWvq2Lw5hOnTU0/IgR0aXYkSJXj8+DFOTk40bNiQNm3a0LhxY7OE74+FH35wIjTUNH/HwJ49ofz+u5aFC6URR2fPaujZ04W//w61+BatL1sWzeHDxmX1uXNEt22bYnNXSBs0O3daXG+36dLO8l9v32LWobpePSuCTvZ77st8dvAJf9PCuO5HfqAJu6hMAFGffYaYJYv8MAmyerWDWVWSUaMiKFRI+oD08RHx8Xn3iMjU5rNvtEz7JYwwMUYTfkwu1tCJ9jaYLi35594nGjbUcelSMI8fC9y8qeLmTTUqFZQtq6d4cb1Vy1ZKY7NGV7x4cebPn8/169dZtmwZLVu2/GiF3H//ubJxo/QbGz06kkKFDEybFsHmzSFmbzUnTmiYO9fyt6xodB8+wpMnaKx8r/K8uESPZWdVlCNHNJKaiYULW9cS5IJOAP7INIzsxLeb0aNhHGMQHR2J/Ppru+au18OPP0rPUaWKzixq730mUyaRnrn2SNbNZJBNGt2RI9IXklq13j9Br1LFdDeoU0dP795R9OoVRdmyaSfkwA5Bt3btWjp06ECG9G47SAX27s0kWS5TRkePHvE/1Dp19Bw/HkL16tK3sR9/dOLiRfOPXCePvLxwIaZRlsIHg2bPHqvbVPfvx0gAG7E3veDAAdvMlmAu6AA8vmzNQq/vJOv20IgHbfvb3S37yBE1T57E/wacnETmzg23aOl4n/mq4SUE4n/DZynHfXXeBPcJDITz56UfRI0a75dGl15Jv53y0jFHj3pIlv/3v0izH2qmTDE+B9NEyuhogS+/dDFLPRB9fDCYhL8JQUGoTOoIKrz/OMgqiZgi6HQIFoJULKLXI5jk4QGJmg7lgq5+/QQenhY6e0c3bUrD7lkpRXxbHj0a1uQeasOEpWzYIH2tb906OkVy2tKavDVyUI3jknUH7vgmuM+xYxpJflmxYvp0EU36IWBV0GXKlIksWbIQFdsMNFOmTGTOnDnBvyx22urfR27eVHH/fvxbr4ODaNXfkSOHyMyZ0lDpy5fVjB8ve2tWAlI+bIKC0Bw6JFllyJxZsmyrn054/RrBJP3E4OFBQjah27dV3L0b/xam1YpUq2Zd0KnCzUP7DaVKEd2pE13UayXr1+61r/pQZGRM6xlT4hLVPzT0JUvSCKkWv/9CwlGX8ga475t/Lj1jNRhl+PDhCIKAJrb7YNzyx86uXdKPrEYNnaSql5xWrXR06BDFunXxD6O5cx2pW1dHgwbxN7K+TBkcDh40LqvPnSO6fftkm7dC2qH96y8EkyLMhty50VWpgvavv4zrVPfuYYvx0sxsmUipO7k2V7WqHldXK4OBoIoVybF0qXE5sk8fEAQM+fPTakZFRg40IMa+H//7r4abN1X4+tqmkR04oJFEWmbJYqB27Q/zYW4oUIAGHv8wJjB+nf9RZ/T6YItm2kePBFavlr6wvC9pBe8DVgWdPHctoVy2j4k9e6RvpI0bJ34zTp0azvHjGh4+jFeg+/VzZt++EDZvdmD1ai0eupEsZA/FY6tRKBrdB4Iool2yRLIqqksXMx+srQEp9ubQ7dsn/YnXq5ewBhVcrhxRHTrgsH49+lKliDSpxenVox411hs4ciT+Pl63zoHvvrOtDNCGDdLfTqtW0Tikz/Zl745aTfHpnfH4PJDA2ITxN29UXLigpmxZ6SuNKMLQoc4EB0sbxyoaXfJhs49uypQpXL582er2K1euMGXKlGSZVHrl7VvMqp3YIug8POCPP8JQqeJNTi9eqChbNiNjxjhz9aqakzez05SdvCLGpKUOCEC4dy9Z56+Q+qjPnJE01BVVKqK6d8eQN69knK2mS5UdOXSRkeZVNhL0zwFoNIT//jtBz54R6u9vJkg7dJD3H3OwqZBPaCjs2CGVam3afJhmSyNtW1KrhYtklTyfEWJq5e7caZ5ukZClSME+bBZ0kydP5tKlS1a3fwyCzt9fmsxZqJCefPlsM9tUrapn+HDpm6+8sOkD8tCd5RgQEHQ6HD+iBPwPFe3ixZJlXaNGiLlymQu6JGp0CZX/OnFCLcn1zJnTQPHiNgZ+ODhYrMvo5xctqUt4966af/5JPGRy924HwsKkc6la9f0LnbeXuvWk13jggIZLl1Q0b+5K+fIZ8PNzZfBgaYWYihV1fP75h5NukR5ItqjLkJAQY43KD5Vdu+w3W5oydGgkVasmvM8Omhnbe2hXrEB4kroNDBWSkcBAHDZtkqyK+uwzALOegzYHo9hR0HnvXun9Wr/+uxcHdneHpk2lmti6dYn/7teulY5p3TraUoDnB0fdutLP6tQpNS1bunL0qIZbt9QcPqyRtE1ycBCZNevDS7dIaxKsjPLff/9x8eJF4/KJEyfQ6cwf1IGBgSxevPiDbsyp15v7Oxo1ss/0otHA77+HUatWBt68ibm5nZxEvLwMksi475lIdY5RI+oYjnPmEDFp0rtfgEKqo127FsEkitHg7Y2uQQMARC8vREdHhNhcE1VgYEwilbzKsgx5QeeETJfy+7VBg+QxFbZvH83mzfGBEzEC1Xp37kWLtGaVUNq1+8DNlrHkzSuSP7/e2IYoOlrg5UvrbxsDB0ZSrNiHl26R1iQo6P7++2+jOVIQBJYsWcISmWM9jrQsrpwa/Puvmpcv49+83NxEqlSx3/SSO7fIrl2hzJunJWtWkV69ojAYoGbNeOGnR8PnLOISxdEuWULk4ME2Fe5VSEeIIlqT6EWAqO7dMb6qq1QY8uZFfe2acbvq7l0MiTQwFWTlv6xVRbl/X5AUHFerxWSLcKxXT4dWKxIVFfPAvndPxd27Annzmjvrdu7UMGyYNJ2mWDE9Zcp8+GbLOOrV01nstyendGk9Q4fa2N9LwS4SFHQ9e/akSZMmiKJIvXr1+O6772jYsKHZOFdXV/Lly2dMRfgQKVpUz/Lloeze7cDOnQJ16ohJjhgrXNjAzJnSN+AFC8Lp0CE+7vs6hVlDJ7qG/4l23jwif/jhXaavkMoIjx6hvnLFuCyq1UR16yYZkxRBp3r0SLJsTaPbt096c1aqpE9MWbQZZ+eY4x09Gv97P3JEQ968Ui3t2DE1n3/uIvFFu7jEVEL5mDKV6tbVmdW/hZjAntato7l3T4WLi0jr1tF8pFUVU5wEJZOXlxdesfXZtm3bRpEiRcj6kWoWGTKAn58OPz8d167dwNMzec20jRrp+PTTKP78M94k9CM/0JnVaJcvJ3LUKJuaNiqkD1RPn0qWDcWLm5XLMvj4SJbVd+6QoM4VFoZKFvlssOIu2LtX+tNu2DB5Q9Vr1dKZCbpu3WIE3YMHAj/+6CTJHQVQqUQWLw4zC6//0KlZU4daLUrqjTZoEM3cueEfbnpFOsNmd7Cvry/Xr1+3uv348eM8e/bM6vYPCZUqUVdKkhg2LAK1Ot78E6fVqV68QNKXXiHdY0t0pL0BKerz5xFMfOT6fPksanRRUeZV8JPLPxeHPMfr8GENoggbNzpQsWJGMyEHMG1aBE2afHy5YW5u0KlT/OdfrpyOpUvDFCGXitgs6H744Qd+/PFHq9snTpzI6NGjk2VSHyt584qSHwTEaHV6VKgeP06jWSkkBVuiI+3NpVOfPi1Z1lesaHGcPK3Ay8tAyZLJG+BQrpweV9f4l7KnT1Xs36/hyy+diYgwtzwMHx5Br14fb8j8zJnhzJsXxuzZYezYEZru++p9aNgs6I4dO2bRPxdHgwYNOHbsWLJM6mNm6NBIiVZ3jSKspaMi6N4zzAovWxJ0co0ukVw6TUCAZFlfoYLFcfv3J39agRytFrNUmb59nYmOlp6oWDE9mzaF2lw95UPFwQE6d46me3fFD5cW2CzoXr16RWZZIVpTPDw8eCEz1yjYT758Bjp2lGp13zKF51ffpNGMFJKCWRqAJUEn89EJDx/GpBhYQhRR//OPZJU1je7oUWmEn2lN1eREbr40jUoG+O67CI4cCUmwLZCCQmpgs6DLkSMH586ds7r93LlzZEukL5aCbQwdGolaiHfYPyQ3n85rYNbeRyH9YlNzVGdn9IULx+9jMOCwf7/l4z16JAlwEZ2c0JcoYTYuONi8p5m8L2JykVAtxrx59RbbVykopAU2C7oWLVrw559/snXrVrNtW7ZsYdWqVbRo0SJZJ/exkj+/ga9rSYs6n3iUlyFDnG2qK6iQ9thiugTQNWkiWdbs2mVxnJl/rmxZLEUz/POPtJt4oUJ6smdPmZumZEkD7u6Wjz14cKQSbKGQbrBZ0A0bNozChQvTs2dPqlWrRu/evenduzfVqlXjs88+o2DBgowYMcLmE+v1eiZMmECpUqXw9PSkVKlSTJgwQVJ5xcPDw+Lf0KHxDR/79etntr1BbPWJ95lxX9yiPvsk61au1LJwYRr2o1ewGVtMlwDRMkHnsGcPRJtHSNrqn5MXHU+o99y7olZb7oDt7W0wC6pSUEhLbBZ0bm5u7Nmzh2HDYuowbt++ne3btwMxver27duHu7u7zSeeOXMmCxcuZMqUKQQEBDB58mT++OMPZsyYYRxz7do1yd+aNWsAaNWqleRYderUkYz7y6TP1/uKKncO1tEBX25I1k+c6EjUxxu89t4g1+isFV/WV6okacIqvH2L+uRJs3FyjU5nxT937Jg0raBatZTNWbNkvhw8ODKhXrAKCqmOXaVMXFxcGDlypNXedCEhIWSwMW42ICCAJk2a0LRpUwB8fHxo2rQpZ86cMY7x9JR25N2xYwe+vr7UqFFDst7R0dFs7PuOmCsXmXnDVvyoRAAhxPTsCAxUcemSeU8rhZTH8Zdf0C5Zgr5sWcJnzkTMlMnyQFE0Ty/IksXyWLUaXaNGaGNf4gAcdu1CX7Nm/JjISNTnz0t2sxSIEhEBZ86knkYHUKeO9Pg5cxr49FPlTUwhffHO9cP1ej27du2iV69eFCpUyOb9qlSpwtGjR41J6FevXuXIkSNWUxhCQkLYuHEjPXr0MNt24sQJfH19KV++PAMGDPggoj/FLFkQtVqKcpVG7JFskz/MFFIe9ZEjOI0bh+r+fRy2bEH7xx/WB4eESDqKi46OJJQ4FR37sheHZudOTJ2x6v/+MxZ/hpji0PIqKwCnT6uN9ScB8uQx4O2dsk7dwoUNfPFFzNy0WpFffw3H0bzalYJCmpLk4pQBAQGsW7eOzZs38+rVKzJkyECjRo1s3n/QoEGEhIRQuXJl1Go1Op2OoUOH0rt3b4vj169fT1RUFJ07d5asb9CgAS1atMDHx4f79+8zYcIE/Pz8OHjwII5WfnE3btywuN4ekuMYiVEiWzacHj2iMqfYSFvjen//UGrXvpvi55eTGtec3oi75pJffilZr1q4kButW1vcR/vwIaVMlqM8PLhx86bVc6h8fCjj4IAq1jenvn2bB/v2ERGbUJ59+3ZMxWRgkSLctvBdbNuWA0xGliz5mhs37lo9rzXs/Z779IEWLbS4u+twcTHwPt4mH/O9/SGQWOccuwTdzZs3Wbt2LevXr+debPfrpk2b0qtXL2rWrInWDsP8xo0bWbNmDQsXLqRIkSJcvHiRESNGkCdPHrp37242ftmyZXzyySdmtTbbto0XAMWLF6dMmTKULFmS3bt34+fnZ/Hc79pO6MaNG6nSkkjj4wOPHlEJaSDC9euZKFgwdUPaUuua0xPGa46IwFGWsK99/tzq56GWlWvTeHkl+tnpa9ZEdeCAcbnAlStExVo3nGWd5p3r1rV4vGvXpN2sGzd2sfs7S+r3/D7fGh/1vf2RkKjp8sWLF8ybN4969epRqVIlFixYQJUqVZg+fTqiKNKxY0fq169vl5ADGD16NF9//TVt27alePHidOrUia+++opffvnFbOyFCxc4e/asRbOlnBw5cpAzZ05u375t13zSI4ZcuQCowGlUxPvkbtxQW80rVkh+NHv3mq3Ty6qamGJW59KGQujyNAOHnTuN/1fL8lctRVxGR0NAgPS9tXp1xY+roACJaHTt2rXj0KFDaDQaGjVqxKBBg2jcuDGOjo7cSaRcUWKEhYWhlmWTqtVqDAbzmnzLli3Dx8eHOnXqJHrcV69e8eTJkw8iOEXMmROADIRSnEtcNDGInT2rUSpOpBIOGzearRMSKLJtTxfwOKKbNEEcPoY/+IJwnOl7cgFCSAhoNGalwSwlip8/ryYsLN4/5+lpIH9+pYGnggIkIuj279+Pj48Ps2fPplatWsl64iZNmjBz5kx8fHwoUqQIFy5cYO7cuXTq1EkyLiwsjL/++osBAwYgyAr2hYSEMHnyZPz8/PD09OT+/fuMHz+ebNmy0bx582Sdb1pgiBV0AJUIkAi606fViqBLDUJCcLCQxC0EBoLBENPKQr7NxmRxU9645aGd40lOR8Z8xyvFruw7dxPXTA4IJi9/Bm9vcHU1299S/pzS1UlBIYYETZdDhw5FEARatWpF+fLlmTRpElevXk2WE0+dOhU/Pz+GDBlC5cqVGTVqFD169OAHWYPRjRs3Ehoayqeffmp2DLVazeXLl+nSpQsVKlSgX79++Pr6smfPHjJmzJgs80xL4kyXAJU5Jdl2+rQSeZkaOOzciRAebrZeMBggKMjiPrYmi8cRGAitW7sahRzAJUowZLwnallrLH2RIhaPYdobDlI+f05B4X0iQY3u+++/5/vvv+fUqVOsW7eORYsWMW3aNIoWLUqNGjXMNCx7yJgxI5MnT2by5MkJjuvatStdu3a1uM3Z2ZmNFsxKHwpiAoLu33/ViKLSizWlcdiwweo21evXGCw0JrSpzmUscULu7Fnzn+KqgCLUyXqRL0yPZSGFR6eDEyfk/jlF21dQiMOmPLrKlSszffp0rl27xsqVKylYsCDLly9HFEV++uknpkyZwnlZQqvCu2NquizOJVwJMS6/eKHi/n1FyqUk6rdv0VgpsgwgvH5teb0dpsthw5wtCrk4Bu1uwWWKGpdNi0DHce6cmuDg+Hsha1YDRYsq/jkFhTjsShjXaDR88sknLF26lOvXrzN79myyZcvG1KlTqVu3LiUsOMkVko6YLRuiJuYhqMZAec5Itp85k+Q0SAUbcDt1CsFC3ck4rAk6W02XwcGwfr00TaQiATgTZlwO0zvRhVUYiBFkljS6I0ek90HNmop/TkHBlCRXRsmYMSPdunVj69atXLx4kTFjxthV61LBBlQqSQUMufnyQ6iQIrx6FVNaa84c673Y0ginBw8S3C68sdwj0NY6l2fPqhHFeInk7a1nn/YT5vKVZNx5ynCQOjHHsqDRHT4svQ9q1lT8cwoKprxzCTCAnDlzMnDgQKXDeAqQUEDKey/oRBGXrl1xGjcO5x9+IEOdOqiSKdgpOdA+eZLgdosanR11LuUaec2aelwL56QnS2nFJsm2JXyGIVs2RFnz46goOHXKXKNTUFCIJ1kEnULKIU8xMOXcObWlji7vDeojR9CcOBG/fPcuGRo2RLNnTwJ7pR5ak0anAPqSJSXLFgWdHXUu5ZGzFSro0RctigB8xVzJtg205U3+smbHOHNGmj/n5WXA11fxzykomKIIunSOaCLovHmIV4b4kPaICIErV97fr1C7dKnZOiE4GJeOHXFYtSr1JyTDUabR6UuXlixbMl2aBaJky2YxNFYUzTXy8uV16IsVA6AeB8hDfOmvcFxYq+lidhy5f65WLcU/p6Ag5/19Sn4kmGp0AlAh8y3J9nPn3k/zpfDiBQ7btlneJoo4/fADadpOXRTRPnsmWWWLoFPZaLZ89Ejg6dP4n5+Tk0jx4gYMRWMiLFWI9GCZZJ/lD8wbCssFnaVGqAoKHzuKoEvnmProAMprL0qWz559PwWdw59/SiIaDZkyIZqoIqpXryCBMlspjfDiBSqT1jiimxuG2G4CxjEWTJe25tDJtbnSpfU4OIC+aHwqQU+WSsacup+L69fjf7IRERAQID2OpUaoCgofO4qgS+eIMkFXUXdCsvzvv0lPMRBevYrpXG3yQE8VDAYzs2Xk4MEYfHwk61TPn6fipKSoZBGXBm9vs0AQlSVBZ2NqgTwQpXz5mEhJMXduxFifXn7uUJuDknGrVsWnIwQEqImMNI3aNODjk4ZasIJCOsVuQXf79m3mz5/Pd999x3fffcf8+fM/iE4B6RVT0yVAhbcHJMuXL6swiX2wGdWFC2SoXJkMDRqQoXZthEQiDJMTzaFDqO/eNS6LWi3RXbogygpxCzLTYWoiyAVd7txmHcUtanQ2JotbCkSJOYAg0eo+Y4lk3KpVWp4/jxFuO3ZIc/AU/5yCgmVsVgf0ej3ffvstS5YsMesw8P3339OzZ0+mTp1q1pFA4d0QPT0R1WoEfcyD0PPNdXJ763jwMOari44WuHRJbdQIbEKnw6VfP6M/SX31Ki7duhH699/g5JTs1yBHu0T68I5u1Sqmo3r27JL1qufPSauMMDONLnduDDKNTrCQ92fmo7Mg6HQ6c99q+fLxJkdDsWLwzz8AtGM9X/MrIcTUbn3+XEWrVq60axfN/PnSxsJKWoGCgmVs1ugmTJjAokWLaN++Pf7+/ty/f5/79+/j7+9P+/btWbJkCRMmTEjJuX6cqNVm5svyBaW+K3v9dNo//kB96ZJkneb0aZyHDk3xABDh1Ss0O3ZI1kX17AmAQa7RpaXp8v59ybIhd25wc0M06VYgBAfHJLKZYNaLzkIwypUrKklKQNasBvLkif/cTTU6V8LoxzzJ/pcvqxk/XvpC4uFhoEkTRdApKFjCZkH3559/0qpVK+bPn0+ZMmXImDEjGTNmpEyZMsyfP58WLVrw559/puRcP1r0BQpIlstllz6E7RF0wtOnOP30k8Vt2pUr0S5caP8E7cBhyxYEXfwDWV+oEPqqVYHYUHwT0lTQyTQ6MXfumEo1cvOlLPLSYnqBDPO0Ar3E5Ggq6AAmMIrGBW9Ynaujo8jixeFkyqT45xQULGGzoAsLC6NGjRpWt9eqVYuwsDCr2xWSjiF/fslyBef/JMv2CDqn0aMRrLSXAXAaORLh3j2r298VeTeA6A4djHlmco1OlZo+Or0e1eXLEGsitmS6BBIXdDaYLk+flnoMjP65uHPF5tLFoSWaP0edpXZtc43N0VFk1aow6tVTtDkFBWvYLOiqVavGyZMnrW4/efIk1apVS5ZJKUiRC7pykdLIy6tXVYSGJn4c9fHjaNetk6yL7NPHGOUHIOh0VvPb3hXh8WPUx49L1kW3aWP8v9xHl2oaXXAwGSpVImO1amQoXx4CA60LOrmfThaQYouPTq7RyQWdmC2bWX1Mh9KFWbUqlKpV4wWaViuycmUY9esrQk5BISFsFnTTp0/nwoULDBkyhGvXrhEdHU10dDTXrl1j8ODBXLx4kRkzZqTkXD9a5IIuy8P/KFAg/uFoMAhcuJC4VqddvlyyrC9RgohJk4j8SlpEWCUrfZVcOGzahGDiA9SVLSu5NnnUZWppdNqlS1HfiknEV9+9i9PkyRKtV9RqjUI4wchLC3Uu5Xl0r18LXL0q/dmVLWsuqKJ69DD+X1erFmLevLi6wvr1oQwfHkHnzlHs2hVKw4aKkFNQSAyboy4rVqyIKIpcv36dJUuWGJuuirEPLo1GQ8WKFSX7CILA48ePk3G6HydyQae6c4dyVfXcuhUv3M6eVVO1asIxiuqL0mTziNGjQaMxaitxpJQm5SBrkhvdtq1k2SDX6GSBHSmF+tw5ybLDmjWSZYO3N8QGoSSo0YWGJlrncv9+jaRjQfHieiz0biXy++/RlyuHEBgo0XpdXeG771I571FB4T3HZkHXunXrd+oorpB0DHnzIgqCURtSPXxImeIR/PWX1jgm0VJg0dGobkgDGvSxLyapoUkJd++iOSPtpxfdurVk2cx0+eJFjM8shVNW1FeuSJZVsrQB0eRFwEyjMxlr0T8n+83s2SP9yTVqZKUqtyCg++SThKatoKBgIzYLunnz5iU+SCFlcHRE9PaWJDGX93wAFDcu//tvwsJAdesWgkkovCFHDuNDW+4PSgmNTisLQtFVrWqWNoGjI6K7O0Js6S9Br0d4/dpi5GKyERWF6vr1BIeYarxxGl0QGdlLQwrc1JAvdpvc5Cv/XPV62LdPLugU06OCQkqjlAB7T5CnGJTRXkKlivd33bypTrBvqVxrMQ1hN6tIkgKCzizasl07i+PMculS2E+nunlTku5gCbmgC8eJ6hyjHRsou3ykUXiZpSR4e0uWT59W8+ZN/E/Ow8NAxYpKk1QFhZTGLkEXGBjIxIkTqVWrFnnz5iVv3rzUqlWLiRMnEmhnd2i9Xs+ECRMoVaoUnp6elCpVigkTJqAzeej069cPDw8PyV+DBtIK7pGRkQwbNoz8+fOTM2dOOnXqxKNHj+yay/uA3E/n9vg6RYpIK9QcP25dQVddviw9nqmgy5rVvKByMja6Ex4/Rm1yflGtJtrPz+JYS9VRUhL5C4AlDCYCy5A5M2vpyH/E96abPDmmQom1SM045GbL+vV1aJJeqlRBQcFGbBZ0t2/fpkaNGkybNg2dTkfNmjWpWbMmOp2OadOmUb16dW7dupX4gWKZOXMmCxcuZMqUKQQEBDB58mT++OMPs8jNOnXqcO3aNePfX3/9Jdk+cuRItm3bxqJFi9ixYwfBwcF07NgRvf7DelM25MsnWVbdvm1W8snf3/pTMyGNDgcHs3YyyRkIopK9eBiKFbNqjkzt6igqWwSdzEf3F+0l20+f1vDkiWCxPqYpe/ZIa1MqZksFhdTB5vfJYcOGERQUxJYtW6hVq5Zk26FDh+jWrRvffvst69evt+l4AQEBNGnShKZNmwLg4+ND06ZNOSMLWHB0dMRT9vCL4+3bt6xYsYK5c+dSt25dABYsWEDJkiU5ePAg9evXt/Xy0j0GmelSffs2dfrqWLAgvt7hoUMJaHSyB7qheHHJspg9O5gEUwjPn0uavr4LZiH3Vr5P4zyAKxRhMDMIn1KEH3zVZrlmyYVapulawpAnj/H/rzXZ2Et5szE7dzrwdQKC7vFjgYsX4/2ogiAq+W8KCqmEzRrdiRMn6Nu3r5mQA6hduzZffvklx2XJwAlRpUoVjh49yvXYQICrV69y5MgRGjZsaHZeX19fypcvz4ABA3hhommcO3eO6Oho6tWrZ1zn7e1N4cKFOXXqlM1zeR8wSzG4dYvq1XWo1fF+uuvX1Tx6ZCEyNiwMlUmHCVEQ0BcqJD2+PPIyGTUps2hEK81IIUbQicCn/MkumnLoTj4+/dSF8PBkm44EuUnXbD6CIBH428/mIRqt2bjt2zUJmi737jWvhpI1q1KyS0EhNbBZ0Lm7u+NhKeEnFg8PD9zd3W0+8aBBg+jYsSOVK1cma9asVKlShc6dO9O7d2/jmAYNGjB//ny2bNnChAkTOHPmDH5+fkTG9k97/vw5arWaLLIHZ7Zs2XiehnUSU4K4FIM4hEePcNNGmAUzWNLqVNevSxK1DXnzxiRkmWBWZzIZg0BsqRZinFv27FygFGcpZ1z37JmKzZsdrO6TZEJDJe2CLCHmyAHaeMG22d+ykD58WEPwfWmxbdFEE5SbLZVEbwWF1MNm02W3bt1YuXIl3bp1I2PGjJJtb9++ZeXKlXTv3t3mE2/cuJE1a9awcOFCihQpwsWLFxkxYgR58uQxHqetSUJx8eLFKVOmDCVLlmT37t34WQlmsIUbN6wXyE3NY9hLSU9PHGND2AVR5MGhQ5QsWYOTJ+M1jq1bw6lY8Y5kvyz+/ph+Y0F58nBLNn9vrRYvk+XXV67wVDYmqdfsfeOG5NgvBMHs2HG46XSsxzwi87ffDFSokLyfuculS5i+mkXmzIk6OBhNcLBxXVjWrMbrDgpSc+BQaYvHio4W2BVdm06sBUDv7Mz1ly/h1SuCg9UcOFBKMr5o0dvcuJE+a8Omxb2d1ijX/H5TsGDBBLfbLOgKFiyIIAhUqFCBzp07kz/WlHbr1i3WrFlDtmzZKFiwIJs2bZLs11qWFBzH6NGj+frrr43CrHjx4jx48IBffvnFqsDMkSMHOXPmNDZ6zZ49O3q9nlevXpHVREt48eIFVWMr4lu7lnfhxo0b73yMpKAuVAhMcrXy6fW0aePGH3/Ej/n330z4+mokecpOsnqMzhUrms1fW7iwZDmbTkdGkzHvcs3OssCgzIULS45tihAWzl+YB6pcvJiBiIjClCxpsLBX0nCQmbdV5crFaM0mtT61hQoZr/vPPx3Q6awbQTbTyijo8PGhYKx5eNIkR8LD4/1zXl4GmjXLhSodJvek1b2dlijX/OFjs6Dr06eP8f+zZs0y2/78+XO++OILyTpBEKwKurCwMLMmrWq12qypqymvXr3iyZMnxuCUMmXK4ODgYOyJB/Do0SOuXbtG5cqVbbuw9wh9gQJoDh82Lqtu36bCl3oyZBAJCYmRbM+fq7hyRUWxYvGfo1kgiqw6PqRsLp0tFf3juByUm2vktbht6VIt06cnoZ26FSxFohry5JEUtdaXKGH8/5YtUvNjA/ayj3if8g4+IRItjkQZ/XOvXgnMmydtkPr551HpUsgpKHyo2CzotiVzRfsmTZowc+ZMfHx8KFKkCBcuXGDu3Ll06tQJgJCQECZPnoyfnx+enp7cv3+f8ePHky1bNpo3bw7E+A27devGmDFjyJYtG5kyZeL777+nePHi1KlTJ1nnmx4wC0i5fRsHB6heXcfu3fEPYX9/DcWKxVdBkUcWyvudgTQY5SG5OHS5JGUeCnh7v3vAhJmPLoFKJ5sPZ7e6be1aLWPHRiCznCd9XhZeAKKbNyf6779x2L2bkJIlMcRaFwIDzdM3pjGUxuzmWaxhNhg3/KlLE3YbBd3s2Y4EB8er11myGOjbV6lVqaCQmtgs6BLqRZcUpk6dysSJExkyZAgvX77E09OTHj16MHz4cCBGu7t8+TJr1qzh7du3eHp6UrNmTZYsWSLxEf7000+o1Wo+++wzIiIiqFWrFvPnzzfTFj8ELAk6gDp1pILu0CENX30VK+gCA1GZFNYWHRzMUhUgXvjcwJfynCH4uhvu1UX27QuhYMF3MxcmVtHflK1/O1rdFhIisGGDAz17Jk8yu8UXAAcHwtauhYgIbjx4QMHYkl/+/g5ER8cLrMJcpRQXaMkWfudL4/p59KMxuxFz5+bZM4Hff5dGaA4aFJlsglpBQcE2kqUuw+XLl1m3bh0bNmzgoqxCvjUyZszI5MmTmTx5ssXtzs7ObJRVu7eEo6MjP//8Mz///LNdc34fkQu6uNYydepII/iOHdMQFRUTLCg3zxkKFpREEcYRZ7r8iZEE4wbA27cC48c7sWLFOwRNWGhdY810ee2aiitX4l9Q1Oj4gj+YTz/jukWLHJNF0AmvX0tqU4parfQFwMlJMv6ff6QvTi3YhgC0YrNE0G2lJdMYSn/v3Eya5ER4eLxw9PIy8PnnUSgoKKQuSfYUPHnyhNmzZ1OjRg1q1KjB7NmzyZaSxXcVYtICTBAePoSwMIoUMeDlFa91hYYKxjSDBCuimCBmykSQ2oN1dJCs37bNgStX3sGhFBKCEBlvqhOdnMxSG+KQ+8DqcYARTEYQ4s2nFy+quXfv3btomJktCxYEB+spDKdPSwVdVWKa3zZkL6WyP5FsG8Fk6s1ow7Jl0heKIUMicXF5l1krKCgkBbueYMHBwaxYsQI/Pz9KlCjB2LFjcXV1Zfr06Vy6dIkDBw6k1DwVAJydMfj4GBcFUUSzdy+CYF5O6q+/Yh7atgSixAxUsTZDL0LJYLZpxgzr5sTEsJhDZ6Hdk8EAGzdKBU071uPDfarll/Y0DAh4d0OE2QuAtc8FiIqC8+elgq4yMRGbGvSsbjAfDyHQuM2AmoAr0nY+3t4GundXtDkFhbQgUUGn0+nYvn07PXv2pFChQgwcOBC9Xs+QIUMQRZGvvvqKzz77jBw5cqTGfD96omNLpsWhjTXvtmsnfYhu3+5AaCjmPehkaQSmLNF1s7h+wwYHHjxImrCz1T+3Zo0DV6/GCxMVeloTk6pS1VNaQzUg4N39r6qbN6XzKlLE6tj//lMTGRkvnHN5BJOLeOFbMOQcq8VOCFj2Zbq6iixYEIZj0t8XFBQU3oEEBd3gwYMpVKgQXbt25d69e3z//ff8999/bN++nc6dO6fWHBVMkHfl1uzeDUFB1Mp8kVzqeJ9TaKjAzp0OkkAUQKIRmnLtmooToWUsbjMYBJYt87K4LTFs8c+FhsKPP0p9Yu1YTzZi9q2a4YJkW3JodGafi6wAsyly/1yFQm8ky+oTJ2jCbibxndm+NWroOHYsmOrVP6wi4woK7xMJCrolS5bg5ubGjh078Pf35+uvvyZnMhX6VUga+goVJEWGhYgIHDZvJkP3rnTWr5CM/Wudg1nnALNmp7H8+afUn+ROoGT577+z8OCB/b4xeRcES3UuZ81y5MmT+FtRq9HzEyONy1VV0sTu//5TERJi91Sk85I3SU3AIiH3z1WoITWxqmKv8Vum8A2zAXB3F5k6NZytW0PJm1epaamgkJYkKOg6dOjAq1evaNGiBa1bt2blypW8ffs2oV0UUhpBIEqm1Tl/+y3q27fpwirJ+v0HNLwKjdeURCcnY1dxU6KjYfVq6cN7JoPwzRQvpPR6lZkwtAXVq1eSZXkO3cOHAnPmSG16X/ndJj/xZcyyvb2Fr2+8RqTXC4l2VE90XjKNLqFODWaCrmFGdBUrmo0TgNkM5Fnb3ly/HkSfPkpiuIJCeiDBn+GCBQu4fv068+bNw8HBgf/9738UKlSILl26sG3bNgQLQQUKKU90mzaSZSG2tH8ZzlGU+NwwnU6QRFEacua0GAiyZo0DL17E3wpuvKUD6xhYdKdk3MmTNggXgwHVxYvGyipyjU7uoxs/XhqCny2bgSFfSV+mhOfPqVRJavp7J/OlwWCu0XlZNs2+fClw545JyoNapHRpvdl3YIpbgSyKP05BIR2R6Pums7Mz7du3Z926dVy5coVx48bx7NkzxowZgyiK/PHHH6xevZrXsnqKCimHoUQJi0ElAjHtbUxZRRfj/y2ZLY8eVTNkiLNkXWdW40I49bVHJOtPn9aQYD9bUcSlUycy1qxJxrJlUR8+nGCLnsDAmEAXU0aNiiBjPql5U/X8OZUrS6NK3yUgRXj5EsGkk73o5mY15UGuzZUoYcDFBaJbtZJ0kzAlIX+fgoJC6mOXYSVr1qz07duX/fv3c/r0aYYOHcrDhw/p378/hQoVMjZRVUhhBMGqRiE3Xx6jBveJefAaZOa5y5dVdOniSlRU/ANbQzTfMAcA35DzZM0aH0kYEiJw+bL1W0b977847NkTM8XQUBxnz0ZIwHR56JAGvT7+3AUK6OnaNRoxUyZEk5w2ITiYSiWlTrmAADUJlEVNEEEeiGKP2bJCjIAUc+RAb6VakKkPVUFBIe1JsgehQIECfP/995w9e5Zdu3bRo0cPYxNVhZRHHn0ZRz7uUhVpA9z9xHRaN5hodM+fC7Rv70pQkFQrWcTnFI81f6qfP7PLZChvYqo+f94YqBGHadTlwYPSYzVurEOtBgTBrMh00YwPcXOLD+oIDFRxZ/ZuiLS/bqTqiTTB265AFJNO53JfaRyiotEpKKQrksVVXrlyZaZPn861a9eS43AKNmDw9bUYEAHQmN2S5UPUBqQBF9OmOfLokfTr/2F4IN2Jj9wUnj+nciWpyfDUKesmQ9UdaR881YsXqG5Jc+AMCQi6unXjzyUXPuqnj6lYUTqXc2N3kbFYMRzHjYuxg9qIXNCJVgSdwQD//iudo2mjW52fH6LGXPAbrES2KigopA3JGhOmsfCjV0g5wmfNQl+qFIbcuQmbPdu4vhaHJePiBF2ciS4iIqYTgCmffx7J4BEgOsf764SICCqVCJKMS8g3ppYJNYgxYZoSp9HdvSsN8tBqRapVM/GbyYSP6skTM+3yONVQvXqF0y+/kKFxY9DZ1rXbVtPl3btOEo3Xw8NAgQLx9lIxc2Z09epJj+XlhRKJoqCQvlCCn99jDMWKEXL4MMEXLxLdvbtRW6rCSbTEm/Tuko/75DY+0P/+24G3b+Mf4JkzG5g0KQJBJSBml7bJKef1CI0m3mR4966aZ88sB2HEdVOwhmmdS7k2V6mSXhIPItfohCdPqFxeaqY8RnXj/9XXrqEOCEjw/MZ52qjRLV0qXV+hgt4saFXuK5XXI1VQUEh7FEH3AREX7edMBJWQPvQPUdsYdblypTTSsWPHaKMSYpD5xlzePqV0abmfzoJWJ4qJCzqTOpdyQSfvwCDXslSPH1M+50NUxM/lKkX5gt+JIGby6rNnEzx/HIINProtWzTs3CmN/mzQwFxjjPbzkxTKjura1aY5KCgopB6KoPuAMA2CqM0hybaDqrqIWbJw7158Z4M4unaNr5Mp1+hUNuawCc+fm5kp5cRpnHo9ZnMw9c+BZdOlx9sHlEUqzBbyBdU4zm3yoT5/PsHzG48lz6GTCdWnTwX+9z9pykWxYno++8xCUWYXF0I3byZs1ixCtm4l+tNPbZqDgoJC6mGToIuKiuLYsWPcsuCDUUg/GBIQdIdVdUGlYtUqLaIYb38rV05H8eLxfieDTNAJNuawJabNQbx/7sIFNW/exN967u4iZcpIhakl06Xq4UN+YiROhEu2naUcDdlLxJmric4BzH10okmyuCjCwIHOvH4dPz8Hh4SLMouenkT36IG+Vi2LCfkKCgppi02CTqPR0KpVK6UNTzrHVNBV4zga4huU3tTl49EjwayMV9eu0iam8rB+4ckTSaQhwNmzarOofnl0pSXiksX9/aXaXK1asWkFpmPlpssnTxAePaIh+zhFZQoiTWW5TQEW3aoHQdLgGTPCw1GZRGiKarUkt+/gQY2kWzvA999HULLku3VZV1BQSDtsEnQqlYo8efIQ8q6VdBVSFFNB50oYFTgt2T5unBMPH8Z/5U5OIm3bSs1xZr6xR4/IlUvEyyteskVFCWb92eSpBZaIEygJpRUY5yHX6J4+RfXgAQCluMhpKvBJEamwm84Q9GcS7nBvFoji5YWplJUL4SpVdHzzjdJHTkHhfcZmH13fvn1ZunQpL2QJwArpB3npKbn5ct06qTbn5xeNu3vCx1A9fAhAqVLSlxx5Pp0tpktD1qyEh5vXzLQk6HBxQTSZnKDToT53zrjsRjBz+/+LszpeAD/Cm7XLEta8zFILZAL1wgXpT6J37ygzbVNBQeH9wubEt7CwMFxcXChXrhzNmjUjb968ODtLHfaCIDBgwIBkn6SCbchLT9XmEFMYYXV8r17mmoq8Hma8oAtlz574KMRjxzQSTcdSDp3ZsbNk4dIltaTkmLe3gbx5LQsnQ44cqE26ZagvSPvSZS2WjZ6VLzLveAXjuhn7ytFRj1XhlFBqgSjCxYvSHUuVUvrIKSi879gs6MaOHWv8/9q1ay2OUQRdGuPujpgxI0JwMADVOYYKPQbMn/rDhkVQpYr5Q1xe1UN4/Bj0esqVC5asP35cg04HGg0xqQU2mi4vXJDOpVw589w041xy5EB9NT7ARIiW+hMNuXLxTd9n/HE8Gh0xfrWbITnZti2UVq0sJ48nlFrw+LHAq1empl29JEFcQUHh/cRm0+X58+cT/TtnYlpKDL1ez4QJEyhVqhSenp6UKlWKCRMmoIutbhEdHc2YMWOoVq0aOXPmpHDhwvTu3ZsHsX6aOJo1a4aHh4fkr1evXjbP44NCECSmRzeCKce/siExDUG//95KjUgXFwyZM8eP1+sRnj2jQIFwsmSJf+gHBcX76YQXL4zCFZAUZDZFzJrVzDSYkMZkLZEbQNRoELNnJ2f9gnSVdWyYPtUB0UqvU7PO4iY+Sbk2V7BguGK2VFD4ALBZo8uTzBXZZ86cycKFC5k3bx7FihXj0qVL9OvXD61Wy/DhwwkLC+P8+fMMHTqUkiVLEhQUxKhRo2jXrh3Hjh2TlBv79NNPGT16tHHZycnJ0ik/Cgy5c6M2Ka78DXPowXIgJvjk99/D8PNLuFSW6O0NJm2XVA8fosqUiRo19GzZEi+oDh/WUL683sw/ZyhcGOH5c1SxPemM67NmNdPoEhJ0CXUVEHPmjLFPuroyNP9fLLvdHTH2ve3iZS2HD0dRu7b5sRMyXcrnVqhQGPy/vXsPi7rMHz7+HgYPCCmIOJYcRCFJlDRKBknxDDxmauahtpV8Ujxu4mKK5aFcKzBF7BdrCduv2nzSMpJyXSmvMEFUtDRcU8MISjMEFFcExWHm+QMcZoYBBwOB4fO6Lq9L7u93vnPfg/LhPn6Q47yEaO0afDjlTz/9REZGBoWFhUyZMgUPDw8qKiooKChApVLRvr1lWaizsrIIDQ3Vp/bx8PAgLCyMb7/9FoAuXbqwc+dOo9ds3LgRtVrNmTNn8PX11Zd36tQJlcmy+LbKdDHJn/knDjZlHF36AVOnVeLpefuhOG3PnkbzYTbnz4OTE8OGaUhJqemtpacrWby49kIUbZ8+0K1brUB307EbP/xgeaCrr0dnOMTqHdCFSbmfkUxNNoHExA4EB5fVel19Q5emga5vXwl0QlgDi4cutVotixYt4pFHHiEyMpLXXnuNvLw8oGpDeVBQEO+8847Fb6xWq8nIyNCn9jl9+jTp6emMGTOmztdcrR4ec3R0NCr/9NNP6d27N2q1mhUrVujva4tMA50CmHjfYZZF37QoyAFoXV2Nn1G9IGXYMOOe4MGDtlRU1A50lb17ozU4Fguqzrn88fw9XL9eMyHXvbuWHj3qGGOk/vQ5hu2sHDSIRWwyur57ty2//FJ78q9Wj66eocuqHp0QorWzuEe3YcMGPvzwQ1566SWCg4ONApKDgwPjx49n165d/OUvf7HoeZGRkZSWlhIQEIBSqUSj0bBkyRJmzZpl9v6KigpWrFhBaGgoPQ1+m58yZQpubm706NGD06dP88orr3Dy5Ek+++yzOt87JyfHwlbXrTGe0RScbG3pY1J2zdm5QfXt0bEjhqHuv//5D4SGotOdwcXFj8LCql57ebmCnTsvMPn77zEcLL7g4IDC3p5eBmUVXbrw1d4ioLO+zMvrar316qTR0K+Oa8V2dpyvfq29szNDSceP78nmQQC0WgUbNlxj4cLzNS/SannIJNDlXLuGNieHq1eV5OcP0pcrlTq8vMpb7Pe5KUmb2wZrarO3t3e91y0OdFu3buWZZ54hKiqKSwbzN7f069eP1NRUM680Lzk5mW3btpGUlISPjw8nTpwgOjoad3d3ZsyYYXSvRqMhIiKCK1eu8NFHHxlde/bZZ/V/9/X1pVevXowaNYrjx48zcOBAs+99uw/ldnJycv7wM5qK0kxetg59+jSovu0efNDo666lpfwK3H+/NyNGwMcf11zLze1FZ5O9lS5qNdjZwdq1NfXq1Yvffzde0RkY2KHeeik6d67zmqOfH51uvbZnT1AqWVj5FhEk6u/54gsVsbGduDVlqygsxMYglY+uc2f6VLc1Pd102FJLhw66Fvt9biot+d92U5E2Wz+Lhy5/++03/P3967xuZ2fXoJNTVq1axcKFC5k8eTK+vr5Mnz6dBQsWsHHjRqP7NBoNzz33HCdPniQlJYWuBisCzRk0aBBKpZJcCzYwWyPToUuofZzWbZ9hOnR5vqZXZDp8uX+/LUrTObrevakcNAhNYKC+rOLZZxu0EAWqtiPo6lj2aLQNolMntH378jT/D0cu64svXbIhOblmTrG+zeKmw5b9+8v+OSGshcWBrnv37vzyyy91Xj9+/DhuZn7I1qWsrAylyQ8xpVKJVlszj3Tz5k1mzpzJyZMn+eKLLyxacHLy5EkqKyvb7OIUXffu6EwWBNW3etEc0710NvUEuiNHlJT/t2Z/m65Tp6pjtRQKrqWkcO2jj7i6bx8V058ysxn7NnOGNjZGBy4b1dEkGGu9vLCnjP/Lu0blW7a01281MJ2fq28himwUF8J6WBzoHn/8cd59912jDAaK6p2+X331Fdu2bWPixIkWv3FoaCjx8fGkpqaSn5/PF198QUJCAo899hhQ1ZMLDw/n6NGjJCUloVAoKCgooKCggPLyqtPrf/75Z2JjYzl27Bj5+fl8+eWXPPfcc/j5+aFWqy2ui1WxsakVqEy/vh3dvfeis6n5p2FTWIii+hRnd3cdvXrVBIGbNxVGCVC1np41J/i3b48mLAztwIHk5yuMsnV37qzDw8OCFaB1LEipFeiq75vP31FQ89zjx231R441ZGuBBDohrIfFgS46OhpXV1eGDRvG7NmzUSgUxMXFMXr0aKZNm0b//v3561//avEbr1u3jscff5yoqCgCAgJYsWIF4eHhrFy5EoDz58+ze/duLly4wPDhw+nbt6/+T3JyMgDt2rXjm2++4YknnuCRRx5h2bJljBgxgpSUlFq9xbZE2894CYe2b9+GPcDWttbS/vYGWwWGDTMOAl8zsua9evc2+0jTQ6D796/ExoJ/fea2GOgcHDA9pPNWr7UPufwfdhtd27ChA2g02KalmX3N9evw44/GlZFsBUJYD4sXo3Tu3Jkvv/yShIQEdu7cSceOHTl06BCenp5ER0fz/PPPN2ij9j333ENMTAwxMTFmr3t4eFBiZmGFIVdXV3bv3l3vPW3RjcWLUR4+jE1RERXPPIPWx6fBz9C6uhoNWbY3SFY6dKiGDz6oGR7N4NGa13l6mn2e6bCladbyOuthJtBpe/aslffNMCC+wBv8i8f0X+/d245TY19E/d3nxq+p7umePm2DRlPzPDc3LU5OOoqKLKqiEKKFa9CG8Y4dOxIVFUVUVFRT1Uc0gsqHH+bq99+jKC2tlV/OUqbDne0LCvR/V6tN5ul4hBu0pwMVaHv1Mvu8Ox0aNDe/aDpsCcYBcRj7CXI4zoHSgfqy2O9C+Ywt+q91nTtzc+xYAL77zvi/wYABMmwphDWxeOgyIyODG6bZNkXLZW9/x0EOamcxMOzRubnp6NmzZmjvBh35joeA2j26kydt+J//ac+hQ8bBxNJAZ3bo0syco+HKUgXwUsf1Rtd3MokT9K+qo6srpbt2oXN1RaeDpCTjxTum2c6FEK2bxYFu/Pjx+mO61qxZw969e9v0CSTWzrTXZNijAwgIMO7V3VqQYtijW7TIjqCge1i50s5oIUqHDjruv9/CU1rqGrq8zX0hl7czaJBxHVfyN4r8R1H69ddo/fwASE21NTqWzMZGx+TJxlkShBCtm8WBbufOnURGRtK+fXveeecdpkyZgqenJ8HBwSxfvpzPP/+cIpnUsBq1hi4NenQAAQHGvZ5MhqBTKvUB8rvvlLz/vvlzTwMDNdSR4KAWc3sAzQ1dmiZqtanU8MJs47M2U5hIj+NfEfasJykpVT3M+HjjsywnTLgpqXmEsDIWz9EFBwcTHBwMVC39//7778nMzOTgwYN88sknvPPOOygUCoqLi5ussuLuMdejM0zTaq5Hp+3pyq0IZrhR29ADD1Sydu11y+thZh+d2UBH1XyeYaLWcV6nGEARJ/DTl1VWKsjMtCUz05aQkJu1hlQjI2V4XghrY3GPzpBSqcTW1hZbW1tsbGzQ6XTodLrbnloiWg+duaFLgyRv/ftrse9QM8R3ERU5PaqGL3U62LnTONDNn3+Dw4evkplZSv/+DegxOTigMzkKzNwcHdQOiu2OZrGBKKN9dYZSU43rOHr0TR58UHpzQlgbiwNdVlYW8fHxTJ06FQ8PD4YPH87f//537O3tWbVqFVlZWVZ1SGhbp3N2RmewXURZVgYGvSVbW3ik53mj12S0HwHAt98qOXfOMFO3jhdfvE7fvto6s4nXRzNihP7vlZ6ede7VM124YnvwIGPYy/c8yFJi8bX7yezrbpHenBDWyeKhy5CQEJRKJePHj2fDhg0EBgbiWscQkrACCkVVXjqDk3Bszp1Da5AiKdDxP+wzyFGQWTaIKcBnnxn3lMaO1eDgcOdVKd+wAa2LC4orV7gRFUVdO81NtyIoDx4EYAD/IZZo/vb4MQ7OSyQ83J78fONnDB6sIShIVlsKYY0s7tE9/PDD2NjYkJKSwqZNm3jzzTdJSUmh0OTkemE9TIcIDTeQAwSRafT1oYLeaLUYJWcFmDTpj61i1HXrxvX16ylPTKx387tpj87GZHGU1tOTgQO17NtXyqhRNXVSKHRER9+4o96mEKLls7hH99VXX1FeXs6RI0f0i1C2bt1KeXk5ffr0YciQIQQFBTF16tSmrK+4i0wXfdhUJ2C9JfBKKgrWoqv+fenUeSf27r1mNGxpZ6dj7Ni7s1y/vkStULP1wclJx8cfl/Hhh+3IyLBl3LibjBypqfe1QojWq0GLUezs7Bg2bBjR0dGkpKSQn59PQkICCoWCDz74gLlz5zZVPUUzMN1iYPvllwYXtTidO8kAThjds2qV8TFwISE3sbdvsioauV2WBsM9fkolhIffJDGxnIkTJcgJYc0adAQYVKXByczM1PfqLl68iE6n47777iPQIP+YaP0qAwKMvm6Xmortv/+NJiwMxYULKG7cIIgD+qzeAKdPGx/19UeHLRvC3Ckqhuo6h1MIYd0sDnRPPfUUhw8fpqSkBJ1Oh5eXFyEhIQQGBhIYGIiHh0dT1lM0A83IkWjUamwPHdKX2S1bxtXhw7H5+WcAgjjAZuabfX2nTjrGjLl7vaVbiVoVlbUXleg6dULXvftdq4sQouWwONBduHCBadOm6QObi4tLU9ZLtAQ2NpS/8QYOwcEoqhPi2vzyCx3i4tBW/2IzkZ305Bznqb0Cd/z4m3TqdHfrq+vRwygj+i3aXr1qZTwQQrQNFge6ffv2NWE1REulHTCAiogIOrz9tr6sw6ZN3KxOkGtPGd/iT+Kj/+AHt7Hk5dlw4YICHx8ta9ZYfgJKo9X33ntrrQ4F9IFZCNH2NHiOrqSkhH379vHLL78A4O7uzvDhw3E02F8lrMv15ctRfPIJ7auPd1NUVNC+OvktgIqL/HXqz9ycUd5cVdSra56urvRBQgjr16BAt2nTJmJiYrhx4wY6g+OgOnbsyPLly3n++ecbvYKiBejShXOLFtF71ao6b2kpgaSuLQayEEWItsvi7QUffPABL7/8MgEBAXz00UccO3aMY8eOsW3bNtRqNS+//DL//Oc/m7KuohldCg1FExRU5/UWE+jq2GLQUuonhLj7LO7Rvf322wQHB/PZZ5+hMJjU79WrF2PHjmXixIls3ryZP//5z01SUdHMFArK16/HYehQFBrjlZS6du3qPGj5bpOhSyGEKYt7dLm5uYwbN84oyN2iUCh47LHHyM3NbdTKiZZF+8ADVMybV/uCQlG1A7sFMDd0qVMo0Lq7N0NthBAtgcWBrkuXLuTl5dV5PS8vjy4GiS8tUVlZydq1a/Hz80OlUuHn58fatWvRGPQYdDodr7/+Oj4+PvTo0YNx48Zx6tQpo+eUlJQQERGBu7s77u7uREREUFJS0qC6CMtcX7q0Vplm6NBmqIl55hK16u67Dzp2NHO3EKItsDjQhYaGkpiYyPbt240Wouh0Oj7++GOSkpIICwtr0JvHx8eTlJREbGwsWVlZxMTEkJiYSFxcnP6eTZs2kZCQQGxsLF9//TUuLi5MmjSJq1ev6u+ZNWsW2dnZ7Nixgx07dpCdnc2cOXMaVBdhoXvuoex//9eoqLKeubu7zWyiVtlaIESbZvEc3erVqzly5Ajz5s1j5cqV9K7OCZabm0tRURE+Pj6sXr26QW+elZVFaGioPkB6eHgQFhbGt99+C1QF0c2bNxMZGcmECRMA2Lx5M97e3uzYsYOZM2dy5swZ9u7dy549exg8eDAAGzduJCwsjJycHLy9vRtUJ3F7NydNoryggPZbt1Lp58eNWbOau0o1qhO1Kv77X32RrLgUom2zuEfXtWtX0tLSeO211xgwYACXLl3i0qVLDBgwgJiYGNLS0nBycmrQm6vVajIyMvjxxx8BOH36NOnp6YwZMwaA/Px8CgoKGDlypP41dnZ2DBkyhMOHDwNVwdLBwYEAg3MZ1Wo19vb2+ntE46uYO5fS9HTKExLAJAN4czOdp5OFKEK0bQ3aR9ehQwfmzp3baFkKIiMjKS0tJSAgAKVSiUajYcmSJcyq7iEUFBQA1DpuzMXFhQsXLgBw8eJFnJ2djRbJKBQKunXrxsWLFxulnqJ10d53H8ozZ2q+lkAnRJvW4JNRGlNycjLbtm0jKSkJHx8fTpw4QXR0NO7u7syYMaPJ3jcnJ6dFPKO1aS1t7j5wIO5paQBoO3TgRw8PNHdY99bS5sYkbW4brKnNt5uiqjPQLViwoMFvplAoeOuttyy+f9WqVSxcuJDJkycD4Ovry6+//srGjRuZMWMGKpUKgMLCQtzc3PSvKywspHv1SfTdu3enuLgYnU6n79XpdDqKior095j6o/N2bXHur1W1ecUKyl1cUJ46RcWMGXg+8sgdPaZVtbmRSJvbhrbW5joD3f79+83umatPQ+8vKytDabL/SqlUoq0+Kd/DwwOVSkVaWhoPPfQQANevX+fgwYOsWbMGgMGDB1NaWkpWVpZ+ni4rK4tr164ZzduJNkSppGK++dRBQoi2p85Ad+LEibouNZrQ0FDi4+Px8PDAx8eH7OxsEhISmD59OlAVOOfNm0dcXBze3t54eXmxfv167O3tefLJJwHo27cvo0ePZvHixcTHxwOwePFiQkJC2tRvLEIIIcxr1jm6devW8eqrrxIVFUVRUREqlYrw8HCWGmxKXrRoEeXl5bzwwguUlJTg7+9PcnIy99xzj/6epKQkli5dqh8CDQsLY926dXe9PUIIIVoeRUlJia6ui0uXLuXpp59m4MCB+rKysjLs7OwaPExpTdra+DZIm9sKaXPb0NbaXO8+usTERKOVOZcuXcLV1ZX9+/c3ecWEEEKIxmDxhvFbDI//EkIIIVq6Bgc6IYQQojWRQHcH2tLY9i3S5rZB2tw2tLU233bVZV5env6Q5f9WH5Sbk5ODg4OD2fv9/f0bsXpCCCHEH1PvqksnJ6daqysNTyAxV37p0qXGr6UQQghxh+rt0SUkJNytegghhBBNot4enRBCCNHayWIUIYQQVk0CXQMkJSXh5+eHSqUiODiYzMzM5q5So4mLi2PEiBG4ubnRp08fpk2bxg8//GB0j06n4/XXX8fHx4cePXowbtw4Tp061Uw1blxxcXE4Ojrywgsv6Mustb2///47c+fOpU+fPqhUKgICAsjIyNBft7Z2V1ZWsnbtWv3/XT8/P9auXYtGo9Hf09rbfODAAaZPn84DDzyAo6MjW7duNbpuSftKSkqIiIjA3d0dd3d3IiIiKCkpuYutaDoS6CyUnJxMdHQ0UVFR7N+/n8GDBzNlyhR+/fXX5q5ao8jIyOC5554jNTWVzz//HFtbWyZOnMjly5f192zatImEhARiY2P5+uuvcXFxYdKkSVy9erUZa/7HHTlyhPfeew9fX1+jcmtsb0lJCSEhIeh0Oj7++GMOHz7MunXrjJIbW1u74+PjSUpKIjY2lqysLGJiYkhMTCQuLk5/T2tv87Vr1+jXrx8xMTHY2dnVum5J+2bNmkV2djY7duxgx44dZGdnM2fOnLvZjCYjc3QWGjVqFL6+vrz55pv6soceeogJEyawevXqZqxZ0ygtLcXd3Z2tW7cSFhaGTqfDx8eH2bNns2TJEgDKy8vx9vbmb3/7GzNnzmzmGt+ZK1euEBwczJtvvklsbCz9+vXjjTfesNr2rlmzhgMHDpCammr2ujW2e9q0aTg5OfH222/ry+bOncvly5fZvn271bW5Z8+erFu3jj/96U+AZd/TM2fOEBAQwJ49e1Cr1QAcPHiQsLAwjhw50ur33UmPzgIVFRUcP36ckSNHGpWPHDmSw4cPN1OtmlZpaSlarRZHR0cA8vPzKSgoMPoM7OzsGDJkSKv+DCIjI5kwYQLDhg0zKrfW9v7rX//C39+fmTNn4uXlxaOPPsqWLVv0R/tZY7vVajUZGRn8+OOPAJw+fZr09HTGjBkDWGebDVnSvqysLBwcHIxyeKrVauzt7a3iM2jWND2tRXFxMZWVlUbDOwAuLi5cvHixmWrVtKKjoxkwYACDBw8GoKCgAMDsZ3DhwoW7Xr/G8P7775Obm8uWLVtqXbPG9kLVARD/+Mc/mD9/PpGRkZw4cYJly5YBEBERYZXtjoyMpLS0lICAAJRKJRqNhiVLljBr1izAer/Xt1jSvosXL+Ls7Gy0R1qhUNCtWzer+BkngU7U8uKLL3Lo0CH27NlTKwO8tcjJyWHNmjXs2bOHdu3aNXd17hqtVsugQYP0w+0PPvggubm5JCUlERER0cy1axrJycls27aNpKQkfHx8OHHiBNHR0bi7uzNjxozmrp64C2To0gLOzs4olUoKCwuNygsLC+nevXsz1appLF++nE8//ZTPP/+cXr166ctVKhWA1XwGWVlZFBcXo1arcXZ2xtnZmQMHDpCUlISzszNdu3YFrKe9t6hUKvr27WtUdv/993Pu3Dn9dbCudq9atYqFCxcyefJkfH19mT59OgsWLGDjxo2AdbbZkCXt6969O8XFxUbZaXQ6HUVFRVbxGUigs0D79u0ZOHAgaWlpRuVpaWlGY9qt3bJly/RB7v777ze65uHhgUqlMvoMrl+/zsGDB1vlZzBu3DgyMzNJT0/X/xk0aBCTJ08mPT0dLy8vq2rvLWq1mrNnzxqVnT17Fjc3N8D6vs9QlSzadGRCqVSi1WoB62yzIUvaN3jwYEpLS8nKytLfk5WVxbVr16ziM5ChSwstWLCAOXPm4O/vT0BAAO+++y6///57q1uRVZclS5awfft2PvzwQxwdHfXj+vb29jg4OKBQKJg3bx5xcXF4e3vj5eXF+vXrsbe358knn2zm2jeco6OjfqHNLZ06dcLJyYl+/foBWFV7b5k/fz5jx45l/fr1PPHEE2RnZ7NlyxZWrlwJYHXfZ4DQ0FDi4+Px8PDAx8eH7OxsEhISmD59OmAdbS4tLSU3NxeoGp4+d+4c2dnZODk54ebmdtv29e3bl9GjR7N48WLi4+MBWLx4MSEhIa1+xSXI9oIGSUpKYtOmTRQUFPDAAw/w2muvERQU1NzVahSmP/RvWbZsGcuXLweqhjJiYmJ47733KCkpwd/fn/Xr1+sDQ2s3btw4/fYCsN72pqamsmbNGs6ePYurqyuzZ89mzpw5+oUI1tbuq1ev8uqrr7Jr1y6KiopQqVRMnjyZpUuX0rFjR6D1tzk9PZ3x48fXKn/qqafYvHmzRe0rKSlh6dKl/Pvf/wYgLCyMdevW1fmzoTWRQCeEEMKqyRydEEIIqyaBTgghhFWTQCeEEMKqSaATQghh1STQCSGEsGoS6IQQQlg1CXRCtEKvv/66VexvEuJukJNRhGghLA1cCQkJTVsRIayMbBgXooXYvn270dfvvfceR48e5a233jIqDwgIwNXVFY1Goz/ZQwhRNwl0QrRQ8+bNIzk5WX/uqBDizsgcnRCtkLk5ugEDBuizLwwfPpwePXoQGBjIN998A8CuXbsYMmQIKpWKYcOGcfz48VrPPXv2LM8++yyenp6oVCqGDh1KSkrKXWiREE1HAp0QViQvL49Zs2YxduxYVq9ezZUrV3j66af55JNPiI6OZsqUKbz44ovk5eURHh5OZWWl/rVnzpxh1KhR/PDDDyxatIi1a9fStWtXwsPDaw2rCtGayGIUIazITz/9xO7duxkyZAhQlX7liSeeYOHChRw+fFifTLdLly5ERkbqe38A0dHR3HvvvaSlpWFnZwfA7NmzmTRpEq+88gpTp07VZzgQojWRHp0QVsTLy0sf5AD8/f0BePTRR40yxt8qz8vLA+Dy5cvs27ePiRMnUlZWRnFxsf7PqFGj+O2332olbBWitZAenRBWxNXV1ejrLl26ANCzZ0+j8s6dOwNVOcgAcnNz9TnLYmJizD67sLDQKpJwirZHAp0QVkSpVDaoXKerWnSt1WqBmgzk5rSWJKRCmJJAJ4TQD2va2trq5+yEsBYyRyeEwMXFhaFDh/L+++/z22+/1bpeVFTUDLUSonFIj04IAUBcXBwhISEEBQURHh6Op6cnhYWFHD16lDNnznDs2LHmrqIQd0QCnRACAG9vb9LS0oiNjWXbtm0UFxfTrVs3+vfvz0svvdTc1RPijskRYEIIIayazNEJIYSwahLohBBCWDUJdEIIIayaBDohhBBWTQKdEEIIqyaBTgghhFWTQCeEEMKqSaATQghh1STQCSGEsGoS6IQQQli1/w9bQnMBMbo2GAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 定义绘图函数\n",
    "def plot_predictions(test,predicted):\n",
    "    plt.plot(test, color='red',label='Real Count') #真值\n",
    "    plt.plot(predicted, color='blue',label='Predicted Count') #预测值\n",
    "    plt.title('Flower App Activation Prediction') #图题\n",
    "    plt.xlabel('Time') #X轴时间\n",
    "    plt.ylabel('Flower App Activation Count') #Y轴激活数\n",
    "    plt.legend() #图例\n",
    "    plt.show() #绘图\n",
    "\n",
    "plot_predictions(y_test_actual,test_outputs) #绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE损失值 27.003487024082244.\n"
     ]
    }
   ],
   "source": [
    "import math #导入数学函数\n",
    "from sklearn.metrics import mean_squared_error\n",
    "def return_rmse(test,predicted): #定义均方损失函数\n",
    "    rmse = math.sqrt(mean_squared_error(test, predicted)) #均方损失\n",
    "    print(\"MSE损失值 {}.\".format(rmse))\n",
    "return_rmse(y_test_actual, test_outputs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.6158455251443455\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "r2 = r2_score(y_test_actual, test_outputs)\n",
    "print(f\"R2 Score: {r2}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
